Methodology for the European cluster and industrial transformation trends report.
Date: 2019-02-10
Institutions: Executive Agency for Small and Medium-sized Enterprises
Subjects: aid to undertakings, business cluster, innovation, investment, modernisation of industry, new technology, regional cooperation, regional policy, small and medium-sized enterprises

December 2019 

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 Methodology for the European cluster and industrial transformation trends report  
 
 
 
 
 
 
 
 
This report was carried out for the European Commission by   

Authors:  Emanuela  Sirtori  (CSIL),  Alessandra  Caputo  (CSIL)  and  Louis  Colnot  (CSIL), 
under guidance from European Commission officials. The authors are thankful to Prof. 
Ron Boschma (Utrecht University) for his scientific supervision. 

For further information, please contact the European Commission Directorate-General 
for  Internal  Market,  Industry,  Entrepreneurship  and  SMEs,  Unit  F.2:  Clusters,  Social 
Economy and Entrepreneurship via email: GROW-F2@ec.europa.eu 

Visit: https://ec.europa.eu/growth/industry/policy/cluster/observatory_en 

DISCLAIMER 

The information and views set out in this publication are those of the author(s) and do not necessarily reflect the 
official opinion of EASME or of the Commission. Neither EASME, nor the Commission can guarantee the accuracy 
of the data included in this study. Neither EASME, nor the Commission or any person acting on their behalf may 
be held responsible for the use which may be made of the information contained therein. 

Luxembourg: Publications Office of the European Union, 2019. 

© European Union, 2019. All rights reserved. Certain parts are licensed under conditions to the EU. Reproduction 
is authorized provided the source is acknowledged. 

PDF                       ISBN 978-92-9202-761-2                doi:10.2826/532498                 EA-01-19-912-EN-N 

 
 
 
 
 
 
Table of Contents 

1 

Introduction........................................................................................................................ 1 

1.1  Background and purpose of the document .................................................................. 1 

1.2  Methodological pillars .................................................................................................. 2 

1.3  Overall analytical framework ........................................................................................ 3 

1.4  Structure of the document ........................................................................................... 5 

2  Methodology for the identification and analysis of global megatrends ........................... 6 

2.1  Definition of megatrends .............................................................................................. 6 

2.2  Selection of megatrends ............................................................................................... 7 

2.3  Foresight scenarios ..................................................................................................... 10 

2.4  Analysis of megatrends’ impacts ................................................................................ 13 

2.5  Validation with Delphi surveys ................................................................................... 15 

2.6  Synthesis of results ..................................................................................................... 16 

3  Methodology for the analysis of cross-sectoral dynamics of industrial transformation . 18 

3.1 

Identification of the ten emerging industries ............................................................. 18 

3.2 

Indicators .................................................................................................................... 19 

3.2.1  Patent and co-patenting ...................................................................................... 20 

3.2.2  Mergers & Acquisitions (M&As) ........................................................................... 23 

3.2.3 

Joint Venture and Alliances (JV&As) .................................................................... 26 

3.2.4  Cross-sectoral analysis ......................................................................................... 27 

3.3  Geographical analysis ................................................................................................. 29 

4 

5 

6 

Comparative analysis of transformation trends within and beyond Europe .................. 33 

Analysis of the role of clusters in industrial transformation............................................ 37 

Synthesis ........................................................................................................................... 40 

List of references considered in the literature review on megatrends ............... 41 

List of experts consulted ...................................................................................... 53 

  Emerging industries: NACE definition .................................................................. 55 

European Observatory for Clusters and Industrial Change ..................................................... 63 

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List of Figures 

Figure 1 – Analytical building blocks under Task 1.5 (Trends Report) ....................................... 4 

Figure 2 – Example of visualisation of the megatrend’s foresight scenario ............................ 12 

Figure 3 – The impact of megatrends on the industries and value chains .............................. 13 

Figure  4  –  Geographical  analysis  of  cross-sectoral  collaboration  communities:  an  example 
related to the Digital Industries ............................................................................................... 32 

Figure  5  –  Value  chain  mapping  and  transformation  trends  -  an  example  related  to  the 
Biopharmaceuticals industries ................................................................................................. 35 

List of Tables 

Table 1 – 

List of ten global megatrends ................................................................................. 9 

Emerging  industries  that  will  be  most  affected  by  the  megatrend,  by  type  of 
Table 2 – 
impact – an example ................................................................................................................ 15 

Table 3 –  M&A data selection output .................................................................................. 24 

Table 4 – 

Cross-sectoral linkages for the Advanced Packaging emerging industry ............ 28 

Table 5 – 

Contribution of the methodological tools to the Trends Report analysis ........... 40 

Table 6 –  Advanced Packaging: NACE definition ................................................................. 55 

Table 7 – 

Biopharmaceuticals: NACE definition .................................................................. 55 

Table 8 – 

Blue Growth Industries: NACE definition ............................................................. 56 

Table 9 – 

Creative Industries: NACE definition .................................................................... 57 

Table 10 – 

Digital Industries: NACE definition .................................................................... 58 

Table 11 – 

Environmental Industries: NACE definition ...................................................... 59 

Table 12 – 

Experience Industries: NACE definition ............................................................ 60 

Table 13 – 

Logistical Services: NACE definition .................................................................. 61 

Table 14 –  Medical Devices: NACE definition ..................................................................... 61 

Table 15 –  Mobility Technologies: NACE definition ........................................................... 62 

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List of Abbreviations 

ECCP 
EOCIC 
ESCA 
IPC 
JV&A(s) 
M&A(s) 
NACE 

NUTS 

R&D 
TOR 

European Cluster Collaboration Platform 
European Observatory for Clusters and Industrial Change 
European Secretariat for Cluster Analysis 
International Patent Classification 
Joint Venture and Alliance(s) 
Merger(s) and Acquisition(s) 
Statistical Classification of Economic Activities in the European 
Community (in French “Nomenclature statistique des Activités 
économiques dans la Communauté Européenne”) 
Nomenclature of Territorial Units for Statistics (in French 
“Nomenclature des Units Territoriales Statistiques”) 
Research and Development 
Terms of Reference 

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1 Introduction 

1.1 Background and purpose of the document  

This report presents the analytical framework and methodological tools underlying the 
provision of the ‘European cluster and industrial transformation trends report’ (Trends 
report). 

The general objective of the Trends report is to “identify and analyse […] how clusters 
of related industries are being transformed and where new specialisation patterns give 
rise to industrial transformations and the development of emerging industries” (study’s 
Terms of Reference -TOR). More specifically, the Trends report aims to: 

  Complement  the  analysis  of  regional  specialisation  patterns  and  correlation 
patterns (carried out in the European Panorama of Clusters and Industrial Change 
Reports),  by  identifying  and  analysing  cross-sectoral  industrial  transformation 
trends  and  changing  specialisation  patterns  in  the  ten  European  emerging 
industries;  

  Assess  the  opportunities  and  challenges  arising  from  global  megatrends  and 

impacting on cross-sectoral industrial development; 

  Compare  European  industrial  transformation  trends,  specifically  concerning 
internationalisation processes, megatrends and positioning in global value chains, 
with a selection of international partner countries; 

  Investigate the role that clusters play in cross-sectoral industrial transformation 

processes. 

The Trends report focuses on the ten emerging industries which were defined in the 
European Clusters Panorama of 2014 through a co-location analysis1. They cover areas 
with high development potential for the future of European industries, thus justifying 
continuing  monitoring  and  analysis.  The  ten  industries  are:  Advanced  Packaging, 
Biopharmaceuticals,  Blue  Growth  Industries,  Creative  Industries,  Digital  Industries, 

1 Ketels, Protsiv, Center for Strategy and Competitiveness, and Stockholm School of Economics, 2014. 
2014. 
European 
https://ec.europa.eu/docsroom/documents/7242/attachments/1/translations/en/renditions/native. 

Panorama 

Cluster 

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Environmental  Industries,  Experience  Industries,  Logistical  Services,  Medical  devices, 
Mobility Technologies.2 

The resulting analysis aims to support policy-makers, cluster managers, practitioners 
and companies in their attempts to identify transformation processes at an early stage 
and guiding the policy efforts towards improving the favourable framework conditions 
for the development of emerging industries and the reshape of traditionally defined 
sectors. 

1.2 Methodological pillars 

A  methodology  of  analysis  has  been  developed  to  serve  the  purpose  of  the  Trends 
Report, which relies on a mix of methods, both quantitative and qualitative, and partly 
follows  the  approach  adopted  in  the  2015  European  cluster  trends  report 3 .  The 
methodology builds on four key pillars: 

  Historical,  current  and  future  perspective.  The  team  adopts  an  analytical 
perspective  that  allows  transformation  patterns,  both  existing  ones,  those  at 
early-stage and possible future ones, to be properly highlighted. Long time-series 
of  data  are  considered  where  deemed  useful  to  illustrate  the  evolution, 
stabilisation or reinforcement of certain processes of industrial transformation. At 
the  same  time,  it  is  acknowledged  that  the  methodology  cannot  only  rely  on 
available  quantitative  data,  since  their  lengthy  time-lag  would  prevent  from 
capturing current trends or future developments. For this reason, extensive desk 
research  of  academic  and  grey  literature,  and  consultation  with  informed 
stakeholders  are  employed  to  detect  the  most  recent  trends  and  spot  new 
transformation processes.  

  Analysis  and  comparison  within  and  beyond  Europe.  The  Trends  report 
focuses on the industrial transformation patterns and trends occurring in the EU 
regions.  However,  the  analysis  also  looks  beyond  Europe  in  two  ways:  i)  by 
analysing the impact of global megatrends on the competitiveness and evolution 
of  European  cross-sectoral  industries,  and  ii)  by  comparing  the  industrial 
dynamics and cluster trends, specifically with respect to the internationalisation 
trends, positioning in global value chain and opportunities and challenges posed 
by the global megatrends, of EU regions and some third countries. To this end, 
the  team  draws  upon  forthcoming  information  on  the  cluster  community  and 
cluster mapping in ten selected third countries outside the EU.  

2 The NACE sector codes which are included into the different emerging industries are listed in Annex C.  

3 https://ec.europa.eu/growth/industry/policy/cluster/observatory/previous-results_en 

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  Take stock of and enrich the previous analysis, without replicating existing 
information.  The  previous  European  cluster  trends  report 4 presented  a  wide 
array of findings from a varied methodology. The new report provides updated 
data  and  analysis  and,  as  far  as  possible,  ensures  comparability  with  the 
conclusions produced by the previous report. The same indicators and analytical 
methodologies  are  used  to  analyse  the  cross-sectoral  dynamics  of  industrial 
transformation and the internationalisation of clusters, although some new data 
are  also  considered.  In  this  way,  it  is  possible  to  determine  whether  industrial 
transformation processes previously detected have continued, in what directions 
and  what  their  impacts  on  cross-sectoral  emerging  industries  are.  The  global 
megatrends already identified in the “Summary Report: Cluster International and 
Global Mega Trends” (June 2015)5 are object of analysis of the new report too, 
even if the focus is on their most recent influence on the industry and possible 
new megatrends worth of study. The scenarios presenting a vision of the cross-
sectoral cluster phenomenon to 2025, as illustrated in the “Foresight report on 
industrial and cluster opportunities6” (August 2015), are retained, but an effort is 
made to confirm or better qualify them in light of available new information. In 
general,  analyses  already  conducted  under  the  previous  Observatory 7  are 
replicated with more up-to-date data, as long as they could provide value-added 
for the current report.  

  Reader-friendly  and  policy-oriented  style  and 

language.  While  the 
methodological  design  is  sound  and  solidly  grounded  on  state-of-the-art 
analytical methods, the report pays due care at presenting the main  messages 
and findings in a fluid writing style. Methodological and more technical analyses 
and  considerations  are  included  in  the  Annexes  as  support.  Redundancies  and 
repetitions  are  avoided.  The  language  aims  to  be  precise  but  easy  to  be 
understood by a differentiated audience, in order to make the report a tool to 
inform policy discussion and decision.  

1.3 Overall analytical framework 

In order to achieve the specific objectives of the Trends report, the work is structured 
around four main building blocks, which are presented in this section and summarised 
in the figure below.  

4 https://ec.europa.eu/growth/industry/policy/cluster/observatory/previous-results_en 

5 https://ec.europa.eu/growth/industry/policy/cluster/observatory/previous-results_en 

6 https://ec.europa.eu/growth/industry/policy/cluster/observatory/previous-results_en 

7 https://ec.europa.eu/growth/industry/policy/cluster/observatory/previous-results_en 

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Figure 1 – Analytical building blocks under Task 1.5 (Trends Report) 

Source: EOCIC 

In short, the European cluster and industrial transformation trends report will comprise: 

  The identification and analysis of the most recent global megatrends that could 

influence the cross-sectoral emerging industries; 

  An in-depth and updated analysis of cross-sectoral dynamics of industrial change; 
  A  focus  on  the  EU  clusters  transformation  processes  in  a  global  perspective, 
extending the investigation to the European cross-sectoral clusters’ participation 
and positioning in global value chains, the opportunities and pressures posed by 
global megatrends and the internationalisation trends, in comparison with non-
European countries; 

  Considerations  on  the  actual  and  possible  role  of  clusters  on  the  identified 

industrial transformation trends. 

The new edition of the report puts a higher emphasis on considering and discussing 
the implications of possible future developments with reference to each of the above 
analytical blocks. Hence,  the  future cross-sectoral developments  are incorporated in 

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“External” influencing factorsIdentification of global megatrends affecting industrial transformations•Identification of global mega trends•Validated foresight scenario•Identification of industries that are impacted by megatrends•Review of how mega trends could impact on industrial transformation processesAnalysis of cross-sectoral dynamics of clustering and industrial changes•Analysis of cross-sectoral linkages•Analysis of geographical patterns•Analysis of changes in cluster structureComparative analysisof transformationtrends withinand beyondEurope•Internationalisationtrends•Positioningin global valuechainsAnalysis of the role of clusters in industrial transformation•Support services to facilitate cross sectoral transformation•Support services to facilitate cluster internationalisation•Need for new support services“Internal” influencing factors 
 
 
every stage of the analysis in order to stimulate strategic thinking, vision building and 
inspire forward-looking decision-making.  

1.4 Structure of the document  

This  methodological  report  is  structured  according  to  the  four  analytical  building 
blocks previously identified:  

  Chapter  2  describes  the  methodology  adopted  to  identify  the  global 

megatrends and analyse their impacts on the ten emerging industries;  

  Chapter  3  describes  the  methodology  to  analyse  the  cross-sectoral  linkages 
characterising  the  ten  emerging  industries  (data  source,  search  strategy,  and 
data processing and analysis); 

  Chapter  4  describes  the  methodology  to  analyse  the  internationalisation 

patterns and the participation in global value chains; 

  Chapter 5 describes the methodology to analyse the role of clusters in industrial 

transformation processes; 

It is accompanied by the following Annexes: 

  Annex  A  includes  the  full  bibliographical  references  used  for  the  analysis  of 

megatrends; 

  Annex B includes the list of experts consulted for the foresight exercise;  
  Annex C presents the list of NACE codes that define the ten emerging industries. 

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2 Methodology for the identification and 

analysis of global megatrends 

2.1 Definition of megatrends 

The  concept  of  megatrend  emerged  in  the  1980s,  with  the  seminal  work  of  John 
Naisbitt, “Megatrends: Ten New Directions Transforming Our Lives” (Naisbitt, 1982). In 
spite  of  its  gain  in  popularity  and  its  broad  diffusion,  especially  to  prepare  for  the 
uncertainties of the 21st century and in the field of business strategy, it lacks strong 
theoretical foundations. It has led to competing frameworks, assumptions and goals 
for the use of megatrends, but often lacking explicit definitions. As such,  to provide 
valuable  and  rigorous insights  in  the  context  of  the  European  Cluster  and  Industrial 
Transformation Trends report, it is essential to clarify the  exact meaning behind the 
term “megatrends”. 

The original definition of megatrends views them as “large social, economic, political, 
environmental or technological changes that are slow to form but continue relentlessly 
over several economic cycles” (Naisbitt, 1982). A central assumption in the analysis of 
megatrends  is  that  they  can  be  detected  from  early  weak  signals  (von  Groddeck  and 
Schwarz, 2013), consolidating over time. Regardless of the definition adopted, there is 
broad agreement on the following characteristics of such megatrends (Kersten, 2014): 

  Long term scale (typically at least 25 to 50 years) 
  Ubiquitous (shaping and shaped by different social systems: politics, economy, 

etc.) 

  Global  (affecting  the  entire  world,  though  specifics  might  vary  by  country  or 

region) 

  Robust (able to overcome temporary and localised throwbacks). 

Beyond  these  characteristics,  there  is  no  universal  agreement  on  the  meaning  of 
megatrends.  This  lack  of  conceptualisation  and  its  resulting  inconsistencies  across 
previous studies have led to harsh criticisms, especially from academia, of the use of 
megatrends  for  foresight  analysis  (see  for  instance  Kersten,  2014;  Slaughter,  1993). 
However,  these  sources,  combined  with  evidence  from  the  recent  literature  on 
megatrends,  do  bring  valuable  lessons  to  refine  the  use  of  megatrends  to  inform 
industrial changes successfully:  

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  The megatrends identified and explored in the literature may come with potential 
biases  and  limits,  stemming  from  the  methodology  typically  used:  expert 
knowledge  may  either  under  or  overestimate  obstacles  in  the  processes  of 
change  underpinning  the  megatrend  (Cuhls  and  Kuwahara,  1994);  bandwagon 
effects  may  be  at  play;  major  events  may  disrupt  transition  processes  once 
thought  as  robust  etc.  These  limits  and  biases  can  be  somewhat  mitigated  by 
cross-referencing analyses from multiple sources and periods. 

  In  spite  of  common  practices,  especially  in  management/tech  consultancies, 
individual  technologies  should  not  be  considered  as  megatrends.  Indeed,  this 
contradicts the core tenant of megatrends and poses risks to be overly sector-
specific and short term. However, it can be argued that a combination of relevant 
technologies can form a megatrend, provided that they are coherently interacting 
with  broader  economic  and  social  systems  (e.g.  smart  mobility  rather  than 
autonomous  vehicles  technologies).  In  other  terms,  an  adequate  definition  of 
megatrends would typically allow the identification of the individual, potentially 
shorter-term, trends which underpin the general megatrend. The analysis should 
highlight  the  mechanisms  linking  short  and  long-term  changes,  as  well  as  the 
potential interactions between individual trends.  

  Opportunities arising from megatrends should be balanced with associated risks 

and limits. 

  Overgeneralisation, stemming from particular national or sectoral contexts, along 
with optimism bias, should be avoided or at least accounted for and nuanced in 
the analysis. 

  Even  if  impossible  to  reverse,  megatrends  should  not  be  seen  as  purely 
exogenous forces that agents or policies must endure without any ability to shape 
or adjust them. 

The main aim of the analysis of megatrends in the framework of this study is to explore 
how they could interact with the cross-sectoral emerging industries, more concretely: 
i)  What  is  the  likely  evolution  of  the  selected  megatrends  (i.e.  foresight  scenarios), 
notably by studying the short and long-term trends underpinning them; ii) What are 
the  possible  impacts  on  emerging  cross-sectoral  industries  logically  stemming  from 
the megatrends. 

In the context of this study, specific focus is put on the European specificities of some 
megatrends  while  retaining  their  global-scale  dimension  (e.g.  details  on  population 
ageing as part of demographic shifts). 

2.2 Selection of megatrends 

The  selection  of  megatrends  is  based  on  a  review  of  the  literature,  covering  a  wide 
range of authors, dates and approaches. Criteria considered for the selection are: (i) the 

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consensus in the literature on the relevance of the megatrend; (ii) the availability of a 
foresight scenario for the megatrend; (iii) the pervasiveness and cross-sectoral impact 
of the megatrend; (iv) the period when the megatrend has started to emerge (in order 
to capture megatrends that are recently emerging, in addition to the most consolidated 
ones).  To  ensure  a  solid  level  of  detail,  the  analysis  has  been  restricted  to  ten 
megatrends. It should be noted that there is a wide agreement on the broad content 
of  megatrends,  regardless  of  their  sources  (consultancies,  private  research  centres, 
academia,  policy-makers  etc.),  national  focus  and  methodology  for  the  foresight 
exercise (Delphi survey, survey, document analysis). However, there are disagreements 
in the literature regarding the exact scope of megatrends, that is to say on where to 
draw  lines  between  what  constitutes  a  megatrend  and  a  single  underlying  trend. 
Thanks to the definition of megatrends used in this study, it is possible to define these 
lines in a consistent way that is adapted to the study of industrial changes. As such, the 
ten selected megatrends are presented in Table 1 below. 

As  stated  previously,  the  megatrends  in  this  list  are  mostly  consensual  in  content 
among  the  reviewed  sample,  covering  a  wide  range  of  transformations  (economic, 
social,  technological,  political). 
In  particular,  they  are  of  high  relevance  to 
understanding the transformation processes at play in the ten cross-sectoral emerging 
industries. 

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Table 1 – List of ten global megatrends  

Global megatrend 

Definition 

Technological megatrends 

Automation 

The use of robotics and “various control systems for operating equipment 
with minimal or reduced minimal intervention” (RBC, 2014). 

Mass 
customisation 
and servitisation 

 “Developing, producing, marketing and delivering affordable goods and 
services with enough variety and customisation that nearly everyone finds 
exactly what they want” (Business Innovation Observatory, 2013).  

The “innovation of an organisation’s capabilities and processes to better 
create  mutual  value  through  a  shift  from  selling  products  to  selling 
product-service systems” (Baines et al., 2009). 

The  process  driven  by  new  technologies  such  as  the  Internet  of  Things 
(IoT), augmented/ virtual reality (AR/VR), and human-machine interactions.  

Data analytics techniques and Artificial Intelligence (AI). 

Integration of 
subjects and 
objects 

Data-driven 
world 

Cybersecurity 
and blockchain 

The  process  towards  increased  “protection  of  networked  information 
systems” (Department of International Trade, 2018). 

 A  “distributed  digital  ledger  technology  utilising  cryptography  and 
timestamps to provide a permanent record of various types of transactions 
and  interactions”  (IHS  Markit,  2018).  As  such,  it  is  considered  a  possible 
solution to some cybersecurity and digital trust challenges. 

Socio-political megatrends 

Globalisation 
and geopolitics 

The prominence and transformation of global trade and the most recent 
shifts in international powers.  

Demographic 
shifts 

Population growth, migration trends and an ageing society. 

Environmental and smart economy megatrends 

Green and 
circular 
economy 

The shift towards an economic system that is internationally competitive 
while  achieving  high  social  and  environmental  standards.  It  covers  the 
energy transition processes and the transition from linear to  sustainable 
production models to face resource scarcity. 

Urbanisation 
and smart city 

The  rising  share  of  population  living  in  urban  areas  and  transitioning 
towards  more  efficient  and  smarter  cities,  thanks  to  decentralisation, 
governance shifts and new ICT solutions. 

Smart mobility 

The transition towards new modes of transportation, the transformation of 
vehicles and the related changes in social norms and business models. 

Source: EOCIC 

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2.3 Foresight scenarios 

Once the megatrends are selected and defined, it is possible to study them in detail 
using  foresight  scenarios  and  impact  analysis.  In  the  context  of  this  study,  such 
scenarios are defined as (Kosow and Gassner, 2007): 

  A description of a future situation (conceptual future) 
  Paths of development, logical links and risks/uncertainties, that are underpinning 

the realisation of this conceptual future. 

the 

Scenarios  are  not  exact  depictions  of  the  future,  but  rather  segments  of  reality 
constructed  and  interconnected  by  the  use  of  available  data  and  assumptions. 
Foresight scenarios about megatrends primarily serve an explorative function, that is 
in-depth  systematisation  and  understanding  of  contemporary 
to  say, 
developments, conditions and influences  (Kosow and Gassner, 2007). Highlighting the 
links  between  individual  trends,  transition  processes  at  play  and  uncertainties  also 
allows the identification of alternatives pathways and interventions (public and private). 
As  such,  the  foresight  scenarios  provide  a  basis  for  the  assessment  of  the  potential 
impacts  of  megatrends  on  cross-sectoral  emerging  industries,  by  identifying  the 
prominent  transformation  processes  in  each  industry  expected  in  the  next  decades 
(2030-2050).  These  impacts  can  then  be  detailed  using  the  available  literature,  in 
qualitative and when possible quantitative terms. 

With the aim to devise the foresight scenarios for each megatrend, the methodology 
is principally based on an extensive review of the literature on megatrends. Based on 
research on megatrends in general and for the ten selected ones in particular, over 300 
documents were retrieved and served as the basis of the analysis, ensuring a balanced 
perspective regarding sources, methods and coverage (see the list in Annex A). 

For  each  megatrend,  the  relevant  literature  is  analysed,  and  the  following  steps  are 
taken to construct the foresight scenarios: 

1.  Identification of the space of possible futures; 

2.  Identification  of  the  critical  risks,  uncertainties  and  factors  that  underpin  the 
baseline scenario, allowing the qualitative exploration of diverging scenarios; 

3.  Identification  of  key  trends  and  transformation  processes  suggested  in  the 
literature, using a rationale that is similar to the contribution analysis method 
(Mayne, 2008); 

4.  Analysis and justification of the suggested linkages between these trends and 

processes, in a theory of causes and effects approach (UNIDO, 2004); 

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5.  Generation of a baseline scenario aggregating the different sources (depending 

on the strength of the methodologies and consensus); 

6.  Identification  of  the  linkages  between  the  trends  and  the  key  impacts  on 

industrial dynamics; 

7.  Creation  of  a  narrative  description  of  the  baseline  scenario  (and  to  a  lesser 
extent of some diverging scenarios potentially generated by risks/uncertainties); 

8.  Creation  of  a  graphical  illustration  of  the  baseline  foresight  scenario. 8  This 
illustration aims to present the different transformation processes underpinning 
each  megatrend  over  the  considered  time  horizon,  their  interlinkages,  the 
changes  brought  by  the  megatrend  which  are  expected  to  have  a  significant 
impact  on  the  industry,  either  a  positive  or  a  negative  one,  but  also  those 
changes which are most likely to be affected by potential risks and uncertainties. 

8  Sources  of  the  illustrations  are:  Vectors  Market  from  www.flaticon.com  and  Freepik  from 
www.flaticon.com.  

11 | P a g e  

 
 
                                              
Figure 2 – Example of visualisation of the megatrend’s foresight scenario  

Source: EOCIC 

12 | P a g e  

 
 
 
 
2.4 Analysis of megatrends’ impacts 

Global megatrends effectively give a tailwind  at the back of emerging industries, by 
accelerating the opportunity of change and providing the fundamentals to grow at a 
higher rate for a  long time. Megatrends all collide to impact on the value chains of 
industries, forcing changes. These might include, for example, the opening up of new 
market opportunities and/or the involvement of new partners that have specific new 
knowledge  and  experience  to  offer.  A  representation  of  how  megatrends  can  affect 
both at the level of industry and value chain is outlined in the figure below. 

Figure 3 – The impact of megatrends on the industries and value chains 

Source: EOCIC 

In  the  context  of  the Trends  report,  the  impact  analysis  is  largely  built  on  the  same 
literature used for developing the foresight scenarios. The study looks at five different 
types of potential impacts that are suggested by the literature, namely: 

  Business organisation: megatrends may transform the business organisation by 
fuelling  different  shifts,  such  as  in  business  models,  production  organisation, 
technological  integration,  team/  human  resources  management,  linkages  with 
the education and training system etc. 

  New  markets:  megatrends  may  promote  or  impede  the  development  of  new 

markets (or market segments), by affecting both supply and demand  

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EnvironmentResources& energyefficiencyCarbon foot-printreductionStandards& normsTechnologyAdvanced data analytics(Big data)Internet of thingsNew advancedmaterialsAutomationSocietyAgeingRapidurbanisationSocial mediaGeo-economicdynamicsNew regionalmarketsNew competitorsNew formsof divisonof labourGlobalisationValue chainIndustryCollaboration & outsourcing             strategyInnovationmanagementHuman capital managementInboundlogisticsOperationsOutboundlogisticsMarketingSalesServiceSupply chainmanagementBlockchainArtificialintelligenceRenewablesMigrationProtectionismSmart mobility 
 
 
  Employment:  megatrends  may  affect  the  number  of  persons  employed  in 
technological  advances  or 

industries,  primarily  because  of 

particular 
organisational changes 
and 

  Skills 

productivity: 

skills 
requirements/development and affect the productivity of workers, both positively 
and negatively 

megatrends 

change 

may 

  Global  value  chains  (re)organisation:  megatrends  may  disrupt  existing  value 
chains (including by making previously unrelated industries converge) or create 
entirely new ones. 

These impact categories tend to carry the most important information to understand 
industrial  dynamics  and  are  consistent  with  the  EU  industrial  policy’s  objectives 
(Eurofound, 2018; European Commission, 2018a; McKinsey Global Institute, 2013; PRI, 2017).  

The  impact  analysis  is  conducted  at  the  megatrend  level,  using  the  same  body  of 
literature informing the foresight scenarios (time horizon 2030-2050) complemented 
by  additional  evidence  deriving  from  the  Delphi  surveys  (see  below).  Qualitative 
insights on how the megatrend may lead to the five considered types of impacts (e.g. 
how it might reorganise labour) are presented. The analysis is especially based on the 
expected  consequences  of  the  megatrend’s  underlying  transformation  processes 
(consistently with the foresight scenarios). 

The team has developed a matrix to visualise synthetically the different impacts of a 
megatrend  on  emerging  industries.  While  it  is  understood  that  all  megatrends,  by 
definition, have some impact of any industry, the matrix focused on the industries that 
are expected to be impacted the most in the future in relative terms. A three-colour 
scale is used to visualise whether the megatrends is expected to have a positive impact 
on  the  industry,  because  it  opens  up  especially  new  opportunities  (green  colour),  a 
negative  impact  because  it  poses  new  challenges  (red  colour),  or  mixed  effects 
(orange).  It  is  worth  reminding  that  the  impact  assessment  is  done  in  relative  (i.e. 
comparative) terms across the ten emerging industries. It is therefore possible to find 
empty cells in the matrix (with no red, or green or orange dots), in case the impact of 
a specific megatrend on a specific emerging industry is considered relatively less strong 
than the impact on the other industries. For instance, in the example illustrated in Table 
2  and  related  to  the  impact  of  Automation,  the  assessment  indicates  that  this 
megatrend is expected to have a significant impact for most of the emerging industries, 
but less so for the Biopharmaceuticals and Experience industries, at least as compared 
to the other emerging industries.  

14 | P a g e  

 
 
Table 2 – Emerging industries that will be most affected by the megatrend, by type of impact – an example 
related to the Automation megatrend 

Emerging industry 
affected 

Business 
organisation 

New markets 

Impact dimension 
Employment 

Skills and 
productivity 

Global value 
chains 
(re)organisation 

Advanced packaging 
Biopharmaceuticals 
Blue growth industries 
Creative industries 
Digital industries 
Environmental industries  
Experience industries 
Logistical services 
Medical devices 
Mobility technologies 

Legend:  

Green: 

the megatrend represents an opportunity for the industry;  

Red 

: the megatrend represents a challenge for the industry;  

Orange 

: the megatrend has mixed effects (both a challenge and potential opportunity). 

2.5 Validation with Delphi surveys 

Source: EOCIC 

The Delphi survey  method was developed to refine long-term predictions in policy-
making, by favouring consensus-building between experts (UNIDO, 2004). In the context 
of this study, the Delphi surveys are aimed at:  

  Validating the robustness of the key hypotheses and assumptions of the foresight 

scenarios; 

  Validating  or  getting  new  insights  into  the  potential  alternative  scenarios  or 

wildcards; 

  Contributing to the assessment of the impacts of megatrends. 

Given the vast diversity among the analysed megatrends, six distinct surveys are carried 
out, each one targeted to a group of experts with specific relevant expertise. The topic 
of the six surveys and the megatrends covered by each survey is as follows: 

  Delphi  survey  n.  1  addressing  advanced  production  technologies  and  digital 
trends.  It  covers  automation,  integration  of  subjects  and  objects,  data-driven 
world, cybersecurity and blockchain; 

  Delphi survey n. 2 addressing mass customisation and servitisation; 
  Delphi survey n. 3 addressing demographic shifts; 
  Delphi survey n. 4 addressing the future of globalisation trends; 
  Delphi survey n. 5 addressing cities and transportation. It covers the urbanisation 

and smart city and smart mobility megatrends; 

15 | P a g e  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  Delphi survey n. 6 addressing the green and circular economy. 

Experts participating in the surveys are selected in a way ensuring the presence of all 
relevant  forms  of  expertise  to  meet  the  goals  of  the  survey.  More  specifically,  the 
following criteria are followed:  

  Discipline of expertise 
  Type of institution (e.g. academia, industry, government…) 
  Geographical/national balance. 

The total number of experts who took part in the Delphi survey is 41 (see the list of 
experts  consulted  in  Annex  B).  Two-round  Delphi  surveys  are  carried  out.  The  first 
round  enables  the  collection  of  data  and  rationales  regarding  precise  elements  of 
interest. The second one principally focuses on the review of a synthesis of the previous 
round, building consensus and highlighting lasting points of disagreements (and their 
underlying reasons).  

Depending  on  the  topic,  the  questionnaires  contain  open-ended  items  and  Likert-
scales for assessing consensus. The realisation of the Delphi survey is internet-based, 
using Survey Monkey©, in order to reduce costs and favour speed of realisation and 
data quality.  

2.6 Synthesis of results 

After  the  analysis  of  impacts  on  the  emerging  industries  is  carried  out  for  each 
megatrend,  results  are  put  together  and  compared  in  order  to  derive  a  synthetic 
assessment about which industries are expected to be affected the most by the global 
megatrends analysed, and which megatrends are likely to have the strongest impact, 
relative to the other megatrends. The focus in this final assessment is no longer on the 
different  types  of  impacts  (namely  business  organisation,  market  opportunities, 
employment,  skills  and  development,  global  value  chain  restructuring),  but  on  the 
overall impact that each megatrend is expected to have on each emerging industry. To 
this end, the number of dimensions that are considered to be impacted the most by 
each megatrend on each emerging industry has been computed. For each megatrend 
and industry, this value can go from 0 (i.e. the megatrend is not expected to impact the 
industry significantly) to 5 (i.e. the megatrend is expected to impact significantly all the 
five impact dimensions of the considered industry). 

The  overall  assessment  points  the  attention to  the  size  of  the  megatrends’  impacts, 
without distinguishing between positive or negative impacts. In fact, when looking at 
the aggregate impact, and not to  individual impact dimensions (e.g. changes in  the 
business  organisation  rather  than  changes  in  employment)  distinguishing  between 
positive  or  negative  overall  impacts  becomes  challenging.  The  overall  impact 

16 | P a g e  

 
 
assessment is therefore neutral, as it considers that any megatrend is likely to generate 
overall mixed effects on each industry.  

17 | P a g e  

 
 
3 Methodology for the analysis of cross-

sectoral dynamics of industrial 

transformation 

3.1 Identification of the ten emerging industries 

Emerging  industries  are  defined  as  industrial  sectors  which  are  at  an  early  stage  of 
development and characterised by high growth rates and market potential. They entail 
the transformation of traditional industries by responding to new market demand and 
exploiting  new  key  enabling  technologies.  As  a  result,  emerging  industries  either 
consist of new activity sectors or, more often, are the result of cross-sectoral spillovers, 
constituting  restructured  traditional  sectors  in  which  new  developments  take  place. 
Building on the results of a co-location analysis carried out by the European Cluster 
Observatory  in  2012 9,  in  2014,  the  European  Cluster  Panorama  has  identified  the 
following  ten  emerging  industries:  Advanced  Packaging,  Biopharmaceuticals,  Blue 
Growth  Industries,  Creative  Industries,  Digital  Industries,  Environmental  Industries, 
Experience Industries, Logistical Services, Medical Devices, and Mobility Technologies.  

The  key  assumptions  underlying  the  co-location  analysis  are  that  firms  in  emerging 
industries  i)  attract  risk  capital,  ii)  attract  interest  from  companies  active  in  different 
industrial sectors, iii) are highly innovative, and iv) grow fast. Hence, to capture these 
dimensions,  a  multiple  approach  analysis  is  carried  out  based  on  four  types  of 
information:  firm  capital  raising  data,  cross-sector  investment  data  (Mergers  and 
Acquisitions - M&A -  deals),  firm patenting data, and information on sector growth 
potential.  

Analysis of M&A and equity raising data is conducted to map the emerging industries, 
with the aim of detecting both the major linkages existing between NACE codes and 
the codes characterised by the highest activity in the market (i.e. those with the most 
significant  number  of  M&A  and  equity  raising  operations).  The  assessment  of  the 
intensity of cross-sectoral deals has shown to what extent traditionally separate sectors 
are increasingly interlinked, giving rise to new configurations of industries. Finally, the 

9 European Cluster Observatory (2012). 

18 | P a g e  

 
 
                                              
analysis of cross-sectoral linkages based on M&A and equity investment data, together 
with comparative analyses of data on sector employment, turnover and value-added, 
and consultations with experts and of relevant references led to the identification of 
the  emerging  industries 10.  Annex  C  presents  the  results  of  this  analysis:  for  each 
emerging industry, a table provides the correspondent list of NACE-4 digits codes and 
their description. 

It is worth mentioning that the analysis of cross-sectoral linkages is focused on finding 
emerging linkages between the emerging industry and other sectors, which reflect the 
fact that the industry is moving towards new technologies and industrial sectors that 
are not included in the emerging industry as it was originally defined. The analysis does 
not  specifically  look  at  changes  in  the  intra-industry  cross-sectoral  activities,  i.e. 
changes  in  the  sectoral  composition  of  the  emerging  industry,  for  which  a  new  co-
location analysis would be needed.  

Even if the European Cluster and Industrial Transformation  Trends report adopts the 
definition  of  emerging  industries  previously  developed  by  the  European  Cluster 
Observatory,  this  analysis  might  also  give  indications  about  new  cross-sectoral 
industries  that  are  becoming  relevant.  It  also  points  out  significant  changes  in  the 
sectoral composition of the ten emerging industries, which could be used for future 
conceptualisation and redefinition of emerging industries.  

3.2 Indicators 

Following the previous European Cluster Trends report (2015)11 the analysis of cross-
sectoral linkages is based on three indicators: i) Patenting and co-patenting; ii) Mergers 
and Acquisitions (M&As), and iii) Joint ventures and alliances (JV&As). These indicators 
are  assumed  to  capture  cross-sectoral  industrial  transformation  trends  along  the 
industrial value chain of each emerging industry. Whereas patenting and co-patenting 
activities  reflect  trends  in  upstream  activities  such  as  research  and  technological 
development, M&A and JV&A data reflect the strategic business activities, often with 
an international character, that could take place both in upstream and in downstream 
segments of the industrial value chain. 

For  each  indicator,  the  location  of  actors  involved  in  a  cross-sectoral  operation  is 
investigated in order to identify regional communities and geographical hotspots, i.e. 
regions with a high concentration of cross-sectoral and cross-regional interlinkages. 
The location of inventors is used for patents, while the location of companies involved 

10 For further details see European Cluster Observatory (2012). 

11 https://ec.europa.eu/growth/industry/policy/cluster/observatory/previous-results_en 

19 | P a g e  

 
 
                                              
in a business operation is considered for M&As and JV&As (see section 3.3 for further 
details). 

3.2.1 Patent and co-patenting 

3.2.1.1  Data source and search strategy 

Patent and co-patenting data are a proxy that covers a central dimension of research 
and technological development of emerging industries. Data on patents are sourced 
from the database PATSTAT. PATSTAT contains bibliographical and legal status patent 
data  from  leading  industrialised  and  developing  countries.  It  identifies  patent 
applications by technology field and location, allowing to explore both cross-sectoral 
and geographical dimensions of upstream activities’ dynamics. 

Each patent application belongs to a patent family (DOCDB family), that includes the 
priority claim (or priority patent application) and all the other applications filed later. A 
priority patent application is the first application filed to protect an invention while the 
subsequent patents are called “second filings”. The count of all patent applications in 
the PATSTAT database may lead to multiple counts of inventions since it mixes both 
priority  and  second  filings.  Duplicates  may  arise  because,  under  the  1883  Paris 
convention, a patent can be filed in more than one country regardless the inventors’ 
and  the  applicants’  location,  to  extend  the  invention  protection  to  other  countries. 
Thus, since the aim of the analysis is to count unique inventions, only priority claims 
are considered.  

Each patent application is classified according to the International Patent Classification 
(IPC), which indicates the technology/ies covered by the patent.12  

Only priority claims filed in EU28 between 2000 and 2016 are selected and downloaded. 
To ease the data downloading, criteria on cross-sectoral patents are already included 
in the search strategy. Specifically, data are extracted based on the following patent 
search strategy: 

  Priority claims: earliest filing date = filing date; 
  Period: earliest filing date between 1/01/2000 and 31/12/2016; 
  Country: only applications patented/co-patented in the EU28 Member States by 

 

inventors located in the EU28 Member States; 
IPC:  only  applications  with  more  than  one  IPC  code,  to  reflect  the  cross-
technological and cross-sectoral nature of the patent. 

12 A patent application can be associated to more than one IPC codes. 

20 | P a g e  

 
 
                                              
The  resulting  dataset  includes  data  drawn  from  the  following  tables  available  on 
PATSTAT: 

  Tls201_appln: patent application bibliographical data 
  Tls202_appln_title: patent application title 
  Tls203_appln_abstr: patent application abstract 
  Tls206_person: key data on applicants and inventors 
  Tls207_pers_appln: patent application applicants and inventors 
  Tls209_appln_ipc: patent application IPC  
  Tls229_appln_nace2: patent application NACE 
  Tls230_appln_techn_field: patent application technological field 
  Tls801_country: patent application country 
  Tls901_techn_field_ipc: correspondence IPC – technology. 

Since a single patent application may correspond to more than one inventor and to at 
least  two  technology  IPC  codes,  data  are  analysed  into  two  different  datasets.  One 
dataset contains patent application bibliographical data and technology information 
(IPC,  NACE,  technology  field,  technology  sector)  to  explore  the  cross-sectoral 
dimension.  The  other  one  contains  patent  application  bibliographical  data  and 
information  on  the  geographical  location  of  inventors  to  explore  patterns  in  cross-
regional co-patenting. The two datasets are then further cleaned based on the results 
of the matching operations, as described below. 

3.2.1.2  Matching IPC and NACE codes 

Patent authorities associate patent applications to one or more IPC code. In principle, 
there  may  be  patents  for  any  industry,  including  the  ten  emerging  industries  under 
analysis.  While patents  are classified according to the IPC system,  the ten emerging 
industries  are  defined  at  NACE-4  digits  codes.  Thus,  in  order  to  attribute  a  patent 
application to any of the emerging industries, IPC – NACE correspondence tables are 
created. The attribution of each patent application to one or more emerging industries 
is carried out in the following ways: 

  Attribution based on the strategy adopted by the European Cluster Observatory 
in  previous  analyses.  Ten  reports  on  cross-sectoral  trends  and  geographic 
patterns in each emerging industry were produced in 2014, accompanying the 
previous European Cluster Trends report (2015)13. These reports provided the IPC 

13 https://ec.europa.eu/growth/industry/policy/cluster/observatory/previous-results_en 

21 | P a g e  

 
 
                                              
definition  for  some  emerging  industries.14 For  the  latest  European Cluster  and 
Industrial Transformation Trends report, this matching is revised to account for 
the  most  recent  updates  in  the  NACE-IPC  correspondence  table  and  to  fill  in 
information  missing  in  the  previous  reports.  Specifically,  the  previous  industry 
reports  did  not  provide  the  list  of  IPC  codes  corresponding  to  the  following 
emerging  industries:  Biopharmaceuticals,  Blue  Growth  Industries,  Creative 
Industries, Experience Industries, Medical Devices, Mobility Technologies. 

  Attribution based on the IPC-NACE correspondence table provided by PATSTAT. 
PATSTAT concordance tables are available to match technology classes of patent 
documents  (defined  by  the  IPC  codes)  to  the  industrial  classification  (NACE). 
Since a correspondence of IPC-NACE at 4-digits is available only in few cases, an 
in-depth analysis of the NACE-2 digits or NACE-3 digits composition is carried 
out in order to single out the IPC codes that could  be attributed with a good 
degree of certainty to the NACE 4-digits definition of emerging industries. 
  Attribution  based  on  the  IPC  definition.  In  the  case  of  the  Biopharmaceuticals 
emerging industry, for which no concordance table is available, the full list of IPC 
codes  whose  technological  fields  related  to  biopharmaceuticals  sectors  is 
identified and considered for the analysis.   

  Attribution based on keywords in either the patent’s title or the abstract. In the 
case of the Logistical  Services industry, no concordance table  is available,  and 
the attribution of IPC codes to the industry based on in the IPC definition is less 
straightforward  than  for  Biopharmaceuticals.  Because  of  this,  a  different 
approach is used. A set of keywords related to logistical services are searched in 
the title and abstract of each patent application to identify the patents whose 
technology can be related to the Logistical Services industry. This strategy relies 
on  the  keywords  identified  by  the  previous  industry  report  of  the  European 
Cluster Observatory (European Cluster Observatory, 2015h). 

For two emerging industries (Experience Industry and Mobility Technologies) no IPC-
NACE  correspondence  is  produced,  like  for  the  previous  European  Cluster  Trends 
report (201515). The diversified cross-sectoral composition of these industries and the 
lack of IPCs codes specifically attributed to technologies related to the experience and 
mobility industries, makes it particularly challenging to develop reliable concordance 

14  European  Cluster  Observatory  (2015a),  European  Cluster  Observatory  (2015b),  European  Cluster 
Observatory  (2015c),  European  Cluster  Observatory  (2015d),  European  Cluster  Observatory  (2015e), 
European  Cluster  Observatory  (2015f),  European  Cluster  Observatory  (2015g),  European  Cluster 
Observatory (2015h), European Cluster Observatory (2015i), European Cluster Observatory (2015j) 

15 https://ec.europa.eu/growth/industry/policy/cluster/observatory/previous-results_en 

22 | P a g e  

 
 
                                              
tables. The analysis of cross-sectoral interlinkages for these two industries is therefore 
based on M&A and JV&A data only.  

As previously explained, the cross-sectoral analysis aims at investigating co-patenting 
between  an  emerging  industry  and  sectors  outside  the  emerging  industry.  For  this 
reason, only patent applications whose technologies belong to at least one emerging 
industry and another technological field are considered relevant to the analysis. At this 
stage,  for  each  patent,  there  are  one  or  more  IPC  codes  that  belong  to  emerging 
industry and at least another IPC code that does not.   

3.2.1.3  Matching investors’ addresses and NUTS2 regions 

The cross-sectoral geographical patterns are analysed by looking at co-patents data. 
The list of the regions (defined at NUTS 2 level) where patents’ inventors are located is 
needed  to  run  the  geographical  analysis.  Following  the  previous  European  Cluster 
Trends  report  (201516),  the  inventors’  addresses  are  considered,  as  these  addresses 
show where the researchers have discovered or realised the inventions, as opposed to 
the entities (e.g. firms or universities) that filed the patent applications. 

PATSTAT provides information on the declared residence (long address, country, and 
continent) of the inventors. To obtain the correspondent NUTS2 code, long addresses 
are  decomposed  in  order  to  extract  either  the  postal  code  or,  if  the  latter  is  not 
available, the city. The matching between postal codes and NUTS2 is done using the 
concordance tables provided by the European Commission.17 Co-patent statistics are 
produced by developing the long list of all co-patents and the pairs of regions involved.  

3.2.2 Mergers & Acquisitions (M&As) 

M&As refer to the consolidation of different companies. A merger is a combination of 
two companies to form a new company, while an acquisition is the purchase of one 
company by another, but where no new company is formed.  

3.2.2.1  Data source and search strategy 

The  data  on  M&As  are  sourced  from  the  Zephyr  database,  which  is  the  most 
comprehensive  database  of  deal  information.  It  contains  official  and  unofficial 
(rumours) information on M&A, IPO, private equity and venture capital deals. Zephyr 
includes financial summaries and structures on companies involved in deals. It also has 
links to the detailed financial data on Bureau van Dijk’s company information products. 

16 https://ec.europa.eu/growth/industry/policy/cluster/observatory/previous-results_en 

17 See the following link for further details: http://ec.europa.eu/eurostat/tercet/flatfiles.do 

23 | P a g e  

 
 
                                              
Data are extracted based on the following M&As search strategy: 

  Deal type: Acquisition, Merger, Minority stake  
  Period:  on  and  after  01/01/2000  and  up  to  and  including  20/03/2018 

(completed-confirmed, completed-assumed) 

  World regions: European Union enlarged (28) (acquirer AND Target) 
  Deal type: Acquisition X%, acquisition majority stake, merger 
  Primary  NACE  Rev.2  (4-digits  code):  Acquirer  and  target  belong  to  different 

primary NACE codes. 

Table  3  shows  the  results  of  the  search  strategy,  making  explicit  the  correspondent 
number of operations retrieved from Zephyr for each criterion (column: “step results”) 
and  the  number  of  operations  retrieved  given  the  criteria  selected  up  to  that  point 
(column: “search result”). 

Following  the  search  strategy,  only  acquisition,  merger,  or  minority  stake  deals 
(criterion 1; resulting deals: 1,261,985) completed between the 1st January 2000 and 
the  20th  March  201818 (criterion  2;  resulting  deals:  1,403,330)  are  selected.  The  two 
criteria together lead to the selection of 1,043,947 deals. Demergers, Institutional Buy-
Out (IBO), Management Buy-Out (MBO), Management Buy-In (MBI), Management Buy-
In/Management Buy-Out (BIMBO), Joint Ventures, Initial Public Offering, Planned IPO, 
Share Buy-Back deals are excluded because they do not capture cross-sectoral linkages 
but rather strategic decisions undertaken by institutional investors or managers. Data 
selection  is  narrowed  to  deals  that  are  completed  and  whose  completion  is  either 
confirmed or assumed. Furthermore, only deals where both the acquirer and the target 
are located in a European Member State (EU28) are selected (criterion 3; resulting deals: 
360,832). The three above described criteria together lead to the selection of 252,949 
deals. Acquisitions listed on Zephyr include more scenarios (acquisition X%, acquisition 
majority stake, acquisition remaining X%, acquisition stake increased from X% to Y%, 
and acquisition stake increased to X%) but only the first two are considered because 
they are the sole ones that do not assume a prior acquisition stake by the acquirer with 
respect to the target (criterion 4; resulting deals: 252,949). The four above mentioned 
criteria together lead to the selection of 167,415 deals. Finally, in order to have a cross-
sectoral  M&As  database,  only  transaction  deals  between  companies  belonging  to 
different sectors (i.e. primary NACE codes) are selected (criterion 5). The final database 
includes 103,231 transaction deals.  

Table 3 – M&A data selection output 

Criterion 

Step result 

Search result 

18 The 20th of March 2018 was the latest available date when data were downloaded. 

24 | P a g e  

 
 
                                              
1. Deal type: Acquisition, Merger, Minority stake 
2. Time period: on and after 01/01/2000 and up to and 
including 20/03/2018 (completed-confirmed, completed-
assumed) 
3. World regions: European Union enlarged (28) (acquirer 
AND Target ) 
4. Deal type: Acquisition X%, acquisition majority stake, 
merger 
5. Primary NACE Rev.2 (4-digits code): Acquirer and 
target belong to different primary NACE codes 
TOTAL (Boolean search 1 & 2 & 3 & 4 & 5) 

Source: EOCIC 

1,261,985 

1,261,985 

1,403,330 

1,043,947 

360,832 

252,949 

252,949 

167,415 

167,415 

103,231 

103,231 

3.2.2.2  Matching NACE codes and Industries 

The  Zephyr  database  provides  information  on  the  primary  NACE  code  of  the 
companies (acquirer and target) involved in transaction deals. Therefore, at this stage, 
two types of matching are necessary: it is necessary, on the one hand, to check whether 
a deal could be attributed to an emerging industry, and, on the other hand, to classify 
the other sectors involved in the transaction.  

The  attribution  of  each  company  to  an  emerging  industry  depends  on  whether  its 
primary NACE code is part of an emerging industry. Hence, deals are attributed to an 
emerging  industry  if  the  primary  NACE  code  of  the  acquirer  or  the  target  company 
corresponded to any of the NACE codes covered by that emerging industry (Annex C). 

At  this  stage,  the  M&As  dataset  included  deals  for  which  either  the  acquirer  or  the 
target industry are identified as companies belonging to an emerging industry but no 
aggregate classifications for the sectors of the other firm involved in the transaction 
are available. The previous European Cluster Trends report (2015) 19used data from the 
Thomson ONE database and used the Business Sector classification to group the other 
sectors. To ensure consistency of data and comparability of findings with the results of 
the  previous  report,  a  correspondence  table  between  NACE  4-digits  codes  and 
Business Sector as defined in Thomson Reuters Business Classification is developed. In 
particular, based on the NACE 4-digit definition, a semantic analysis is applied to find 
the  correspondent  Activity  definition  in  Thomson  Reuters  and,  thereby,  the  related 
Business sector.  

19 https://ec.europa.eu/growth/industry/policy/cluster/observatory/previous-results_en 

25 | P a g e  

 
 
                                              
3.2.2.3  Matching companies’ addresses and NUTS2 regions 

The Zephyr database includes information on the geographical location (postal code, 
region, city) of the acquirer and the target company. Using the correspondence table 
provided  by  the  European  Commission, 20  for  each  deal,  the  NUTS2-level  code 
corresponding  to  the  location  of  both  the  target  and  the  acquirer  is  identified.  The 
attribution is done in multiple steps, depending on the available information. When the 
postal code is not available, the matching is made by looking at the city and, in ultimate 
instance, at the region variable.  

The resulting dataset includes cross-sectoral deals involving an emerging industry on 
one side of the transaction and for which the geographical information is available for 
both parts involved in the deal. All transactions with missing geographical data cannot 
be considered for geographical analysis. 

3.2.3 Joint Venture and Alliances (JV&As) 

Analysing  the  sectoral  composition  of  companies  which  have  signed  alliance 
agreements  can  indicate  cross-sectoral  trends.  Alliances  include  joint  ventures, 
strategic  alliances,  research  and  development  agreements,  sales  and  marketing 
agreements,  manufacturing  agreements,  supply  agreements,  and  licensing  and 
distribution pacts.  

3.2.3.1  Data source and search strategy 

Data on JV&As are drawn from the SDC Platinum database, which is part of Thomson 
Reuters. This database contains data on joint ventures and strategic alliances occurred 
since  1988  and  collected  through  the  aggregation  of  several  sources,  including  SEC 
filings, news feeds and so on. An advantage of using this database is that it provides 
data  available  for  the  most  recent  years.  Another  advantage  is  that  it  ensures 
consistency with the methodology used in the previous European Cluster Trends report 
(201521). 

Data are extracted based on the following JV&A search strategy: 

  Database: Joint Ventures (JV) 
  Alliance Date Announced: 1/1/1962 to 31/7/2018 (All) (Calendar) 
  Alliance Date Completed: 01/01/2000 to 31/07/2018 (Custom) (Calendar) 
  Participant Nation: EU28 Member States. 

20 For further information see http://ec.europa.eu/eurostat/tercet/flatfilesChangeNutsVersion.do 

21 https://ec.europa.eu/growth/industry/policy/cluster/observatory/previous-results_en 

26 | P a g e  

 
 
                                              
3.2.3.2  Matching SIC and NACE codes 

SDC Platinum classifies industries using the  SIC classification, which differs from the 
NACE classification used to define the emerging industries. While there are SIC-NAICS 
as well as NAICS-NACE correspondence tables, there is no SIC-NACE correspondence 
table available. Thus, to attribute each deal to one or more emerging industry, a SIC-
NACE  correspondence  table  is  developed,  by  combining  the  two  above  mentioned 
tables.  To  ease  the  matching  process,  NACE-4  digits  definition  of  each  emerging 
industry is transformed into NAICS-6 digits definition and then, based on the latter, the 
correspondent SIC-4 digits definition is identified. 

On the basis of the identified  SIC-4 digits definition of the ten  emerging industries, 
JV&A deals are assigned to each emerging industry by looking at the participants’ SIC 
codes provided in the SDC Platinum database. If at least one of the participants’ SIC 
codes  corresponded  to  one  of  the  SIC  codes  included  in  the  emerging  industry 
definition, the deal is included in the database and classified accordingly. The sectors 
outside the ten emerging industries are classified based on the “Participant industry” 
classification provided in the SDC Platinum database. 

3.2.3.3  Matching companies’ addresses and NUTS2 regions 

The SDC Platinum database includes information on the geographical location (postal 
code and city) of deal participants. Using the correspondence table provided by the 
European Commission, the location of participants to each deal is identified with the 
correspondent NUTS 2-level code. 

3.2.4 Cross-sectoral analysis 

The cross-sectoral transformation trends of the ten emerging industries are detected 
by  analysing  the  cross-sectoral  linkages  between  each  emerging  industry  and  other 
sectors. This analysis is run on the three datasets previously constructed (respectively 
on patents/co-patents, M&As, JV&As). 

Consistently  with  the  methodology  adopted  by  the  European  Cluster  Trends  report 
(2015)22, data are processed to compute indicators such as the total number of cross-
sectoral linkages per year and the growth rate of cross-sectoral linkages over time. The 
analysis is carried out by looking at cross-sectoral linkages starting from the year 2000 
until  the  year  2016,  the  latest  year  for  which  both  patent,  M&A  and  JV&A  data  are 
available. The analysis of the cross-sectoral temporal dynamics is carried out over three 
periods of equal duration: 2000-2004; 2005-2010; and 2011-2016.  

22 https://ec.europa.eu/growth/industry/policy/cluster/observatory/previous-results_en 

27 | P a g e  

 
 
                                              
The strength of the cross-sectoral linkages is assessed through frequency counts, i.e. 
the absolute number of interactions (patents, M&As, or JV&As) between the emerging 
industry under analysis and any other sector not belonging to that emerging industry.  

In order to identify the sectors that are the  most interconnected with the emerging 
industry, a two-step approach is adopted: 

  First, the top 15 sectors characterised by the highest total number of interactions 
with the emerging industry under analysis in the 2011-2016 period are identified.  
  Second, the top 15 sectors previously identified are ranked on the basis of the 
growth rate of cross-sectoral linkages between the last period (2011-2016) and 
the previous one (2005-2010).  

The  analysis  allows  capturing  if  major  shifts  have  occurred  or  previously  emerged 
trends have stabilised, by separately looking at patents, M&As and JV&As. The analysis 
of  the  three  databases  produces  complementary  findings  that  shed  lights  on  new  / 
increasingly  relevant  technological  trends  and  industry  niches  that  are  transforming 
the value chain of the emerging industry. As an example, the following table shows the 
industry  sectors  that  are  the  most  interconnected  with  the  considered  emerging 
industry.  

Table 4 – Cross-sectoral linkages for the Advanced Packaging emerging industry 

Core industry under 
analysis 

Advanced Packaging 

Link to other 
industries showing 
positive dynamics 
related to the core 
industry 

Total period  
2000-2004 

Patent data 

Total period  
2005-2010 
[% Growth rate  
(2000/04)–
(2005/10)] 

Total period  
2011-16 
[Growth rate  
(2005/10)–
(2011/16)] 

Advanced packaging   Materials, 
metallurgy 

Advanced packaging   Measurement 

Advanced packaging  

Electrical machinery, 
apparatus, energy 

Advanced packaging   Mechanical 

Advanced packaging  

Advanced packaging 
acquired 
Advanced packaging 
acquired 
Advanced packaging 
acquired 
Advanced packaging 
acquired 

elements 
Transport 

Power 

Professional services 

Other industrials 

Chemicals 

1276 

1277 

1227 

3026 

3122 

M&A data 

3 

14 

15 

26 

993 
-22% 
1277 
0% 
924 
-25% 
2923 
-3% 
3330 
7% 

15 
400% 
16 
14% 
20 
33% 
29 
12% 

1790 
80% 
1726 
35% 
1180 
28% 
3560 
22% 
3548 
7% 

53 
253% 
46 
188% 
48 
140% 
59 
103% 

28 | P a g e  

 
 
 
 
 
 
 
 
 
 
 
 
 
Advanced packaging 
acquired 
Advanced packaging 
was a target  
Advanced packaging 
was a target  
Advanced packaging 
was a target  
Advanced packaging 
was a target  
Advanced packaging 
was a target 

Construction 
materials 
Transportation & 
infrastructure 
Automobiles & 
Components 
Chemicals 

Professional services 

Electronics 

Advanced packaging  

Construction Firms 

Advanced packaging   Miscellaneous 
Manufacturing 
Electric, Gas, and 
Water Distribution 

Advanced packaging  

Advanced packaging   Wholesale Trade-

Durable Goods 

25 

11 

37 

35 

4 

80 

JV&A data 

7 

8 

4 

5 

Advanced packaging   Metal and Metal 

15 

Products 

Source: EOCIC 

45 
80% 
5 
-55% 
22 
-41% 
31 
-11% 
22 
450% 
100 
25% 

3 
-57% 
4 
-50% 
5 
25% 
8 
60% 
13 
46% 

76 
69% 
36 
620% 
59 
168% 
77 
148% 
52 
136% 
197 
97% 

8 
167% 
8 
100% 
8 
60% 
12 
50% 
19 
46% 

Generally speaking, patents capture the largest number of cross-sectoral operations. 
The  average  share  of  cross-sectoral  patents  concerning  the  total  number  of  cross-
sectoral  transactions  (thus  also  including  M&As  and  JV&As)  is  around  80%  in  each 
time-period. The European Cluster and Industrial Transformation Trends report shows 
aggregate figures of cross-sectoral linkages by emerging industry, on the basis of the 
simple sum of the three types of cross-sectoral linkages (patents, M&A and JV&A). This 
is the same approach adopted to conduct the geographical analysis at the level of each 
emerging industry (see below).  

3.3 Geographical analysis  

The geographical dimension of the cross-sectoral linkages is investigated through a 
network  analysis  at  the  NUTS2  regional  level,  as  it  allows  an  acceptable  balance 
between data availability and precision. Through the network analysis, it is possible to 
detect the regions where cross-sectoral linkages are concentrated and to identify larger 
cross-border or international communities that are the closest linked according to the 
indicators  selected  (patents,  M&As,  JV&As).  Similarly,  to  the  cross-sectoral  analysis, 
also  the  geographical  analysis  is  performed  for  each  emerging  industry  for  three 
different periods: i) 2000-2004, ii) 2005-2010, and iii) 2011-2016. This approach allows 
changes  in  the  geographical  distribution,  spread  and  strengths  of  cross-sectoral 
linkages across European regions to be detected. 

To  conduct  the  geographical  analysis,  the  three  databases  on  patents,  M&As,  and 
JV&As containing geographical data  are firstly appended into one dataset and then 

29 | P a g e  

 
 
 
 
 
 
 
 
 
 
 
 
 
split again by emerging industry and by period. As a result, thirty different datasets are 
created (one per emerging industry and  period). The resulting datasets consisted of 
two-column tables where one column includes the list of the deal IDs and the second 
one the correspondent list of regions involved in the co-patenting activity/transaction 
deal.  

The network analysis is performed separately on each dataset, using  the software R, 
following the steps described below: 

1.  The two-columns dataset is transformed into its correspondent adjacency matrix. 
An adjacency matrix is a square matrix with as many rows and columns as the 
regions in the dataset. The elements in the cells of the matrix count the number 
of interactions (ties) between each pair of regions. Further, since the aim of the 
analysis  is  to  detect  cross-regional  communities  without  any  interest  in  the 
direction  of  the  cross-regional  interaction,  the  network  is  analysed  as  to  be 
undirected. As a result, the adjacency matrix obtained on R is symmetric.  

2.  The  giant  component  of  the  European  network  is  identified.  The  giant 
component is defined as a connected component of a network that contains a 
significant  proportion  of  the  entire  nodes  in  the  network.  Usually,  the  larger 
(higher number of nodes) the network, the larger (higher fraction of nodes) the 
giant component. The giant component can be interpreted as a large cluster that 
includes regions connected one to the other.  

3.  The network analysis is implemented on the giant component through the spin-
glass community algorithm, requiring to use the number of edges between each 
pair  of  regions  as  weight.  The  spin-glass  community  detection  algorithm  is  a 
function that finds communities in a graph (network). A community consists of a 
set  of  nodes  with  many  edges  inside  the  community  and  few  edges  between 
outside it (i.e. between the community itself and the rest of the network graph). 
Generally speaking, a community is a subset of regions within the cluster (giant 
component)  that  are  strongly  interconnected  via  cross-regional  and  cross-
sectoral activities.  

4.  The most dynamic hotspot within each community  is  identified by computing 
the  alpha  centrality  measure.  While  the  alpha  centrality  measure  is  computed 
with reference to the entire giant component, the hotspots are identified as those 
with  the  highest  centrality  measure  within  each  community.  In  other  words,  a 
hotspot corresponds to the region that is the most central within the community, 
in the sense that it acts as a bridge between a large number of regions. It is worth 
noticing that a hotspot does not necessarily correspond to the region where the 
largest  number  of  cross-regional  and  cross-sectoral  activities  takes  place; 
instead, it corresponds to the region that is connected to the largest number of 
different regions. 

30 | P a g e  

 
 
Finally, the intensity of the cross-sectoral activities of each region included in the cluster 
is estimated. The intensity assessment used the frequency counts to classify regions 
according  to  the  number  of  cross-sectoral  linkages.  Specifically,  regions  where  the 
number of cross-sectoral operations is below the average are not taken into account 
because, even if they have connections within the giant component, the intensity of 
their activities is so low that, if included, would lead to misleading conclusions in the 
network analysis. By focusing on regions whose cross-sectoral activity is above average, 
only  regions  that  are  more  active  are  analysed.  In  other  words,  once  the  giant 
component (or cluster) is identified, the total number of interactions (frequency) that 
characterises each region within the cluster is analysed. Firstly, the distribution of the 
frequency is analysed in terms of percentiles. Secondly, all the regions whose percentile 
is  below  50%  are  dropped.  The  remaining  ones  are  classified  as  regions  at  high, 
medium  or  low  intensity  if  the  percentile  of  their  cross-regional  and  cross-sectoral 
frequency is respectively above 85%, between 60% and 85%, below 60%. 

The  results  of  the  network  analyses  combined  with  the  intensity  assessment  are 
visualised  through  maps  at  the  NUTS2  level  of  aggregation.  For  each  emerging 
industry,  three  maps  are  produced,  one  for  each  considered  period.  An  example  is 
provided below. The regions coloured in the map indicate the cluster of regions with a 
number  of  cross-sectoral  and  cross-regional  linkages  above  the  median.  Different 
communities,  detected  through  the  spin-glass  algorithm,  are  indicated  by  distinct 
colours. Within each community, the different shares of the same colour indicate the 
intensity of cross-sectoral linkages for each region (high, medium and low).  

31 | P a g e  

 
 
Figure 4 – Geographical analysis of cross-sectoral collaboration communities: an example related to the Digital 
Industries  

Source: EOCIC 

32 | P a g e  

a) 2000 -2004b) 2005-2010c) 2011-2016Communities and frequencies of cross-sectoralcollaborationsCentral EuropeUK and IrelandNorthern EuropeCentral-Eastern EuropeEastern EuropeItalyand otherWestern countriesPoland (onlyperiodc)Spainand Portugal (onlyperioda)France (perioda and b)Hungary(onlyperiodc) 
 
 
4 Comparative analysis of transformation 

trends within and beyond Europe 

The third analytical block distinguishes from the others in the following aspects: 

  It essentially takes a global perspective, focusing on the cross-sectoral dynamics 
in  terms  of  internationalisation  trends,  transformation  trends  along  the  global 
value chains which European emerging industries are part of, and influence of the 
global megatrends on the industries; 

  It  takes  a  comparative  perspective,  as  it  assesses  the  position  and  trends 
observed for European industries against those of a selected list of non-European 
partner countries. 

The  benchmarking  analysis  with  non-European  countries,  which  was  missing  in  the 
previous  Trends  Report,  takes  here  great  importance  and  aims  to  put  the 
transformation trends observed in the EU from a broader perspective. The comparative 
analysis aims to increase the understanding of the ongoing transformation processes 
in Europe and contribute to identifying (upcoming) priorities to support cluster and 
industrial change.  

Already available information from other international cluster mapping tools (e.g. US 
Cluster Mapping, Russian clusters map,23 etc.) and the forthcoming information on the 
cluster community and cluster mapping in 10 selected third countries developed by 
the European Cluster Collaboration Platform are the main sources of information about 
clusters in non-European countries. The list of partner countries to be considered has 
been  selected  by  the  European  Commission,  and  they  are:  United  States,  Canada, 
Japan, Singapore and the Republic of Korea. These are all important partners for the 
EU,  having  developed  strong  and  long-standing  cooperation  in  trade,  investments, 
science, technology and innovation with the EU Member States. 

focuses  on 

the  analysis 

As  mentioned, 
the 
internationalisation trends and the transformation trends in the global value chains. As 
to  the  first  topic,  i.e.  the  analysis  of  the  internationalisation  trends,  the  aim  is  to 
identify  the  key  drivers  for  the  internationalisation  of  cross-sectoral  clusters  and  to 
assess  the  extent  to  which  industrial  transformation  processes  have  an  impact  on 

two  closely 

related 

topics: 

23 http://www.clustermapping.us/; http://map.cluster.hse.ru/ 

33 | P a g e  

 
 
                                              
internationalisation patterns. The key research questions for this part of the analysis 
are:  

  What is the current position (e.g. leaders, peers, needing to catch up) of European 
industries  from  an  international  perspective,  also  as  compared  to  non-EU 
countries?  

  What  impact  do  global  megatrends  have  on  the  internationalisation  trends  of 

European cross-sectoral industries, also as compared to non-EU countries? 

  How can international cooperation contribute to improve or maintain the current 

position and which are the most suitable international partner countries? 

  Are there noticeable differences between industries, clusters and regions when it 

comes to international cooperation? 

  What are the global market conditions outside Europe and where do areas exist 

that offer new markets? 

To answer such questions, data made available by the European Secretariat for Cluster 
Analysis (ESCA) has been used at first. On issues related to internationalisation, specific 
data has been collected by ESCA since 2012. Notably, the following indicators are of 
interest for the present analysis: 

  Degree of internationalisation of cluster participants (based on the assessment of 

cluster managers; scale from 0 to 4) 

  Characteristics  of  cooperation  with  other  international  clusters  (based  on  the 

assessment of cluster managers) 

  Target  regions  and  stakeholders  for 

internationalisation  (based  on  the 

assessment of cluster managers). 

This data is used to provide an overview of internationalisation patterns, illustrating the 
recent  internationalisation  aspects  of  European  clusters  (particularly  in  each  of  the 
identified emerging industries), and the target regions and intensities of international 
collaborations of clusters. Dynamic trends are pointed out through an analysis of the 
data for the years when they are available.  

The second area of the study refers to changes in the value chain of each emerging 
industry. In this regard, the aim is to answer the following questions: 

  Which are the main actors (final users, manufacturer, RDI actors, etc.) involved in 
the value chain of each emerging industry both upstream and downstream? 
  What is their role in the value chain (financing, R&D, demonstration, innovation, 

manufacturing, sales and marketing, user, maintenance and repair)?  

  What  position  do  European  regions  and  countries  occupy  in  the  global  value 

chain of the emerging industries as compared to third countries? 

34 | P a g e  

 
 
  What  are  the  ongoing  trends  in  the  interactions  between  different  sectors 

involved in the value chain? 

  What impact do global megatrends have on the global value chains of European 

cross-sectoral industries, also as compared to non-EU countries? 

  Which  are  the  areas  in  which  more  added  value  for  European  clusters  can  be 

captured and fostered through public support measures? 

The technique of value chain mapping is used to identify the various actors in the value 
chain, their functions and the interdependences between them. A visual illustration of 
the way how the products flow from raw materials to end markets is produced for each 
emerging industry. The main cross-sectoral transformation trends that are taking place 
or are expected to affect the value chain are also visualised (see for instance the figure 
below). The roles and flows within the value chain are investigated and how different 
actors ‘add value’ to the process and interconnect in new ways is discussed. 

Figure 5 – Value chain mapping and transformation trends - an example related to the Biopharmaceuticals 
industries  

Source: EOCIC 

The analysis relies on the critical analysis and interpretation of the quantitative data on 
cross-sectoral linkages (computed as explained in Chapter 3). Quantitative indicators 
are complemented by qualitative research on various data sources, including: academic 
and grey literature review, in-depth scrutiny of patent abstracts and details of the M&A 
and JV&A deals, review of the cluster facilitated projects for new industrial value chains 
funded by the H2020 INNOSUP call, analysis of the assessment reports of regions in 

35 | P a g e  

 
 
 
industrial  transitions 24  produced  by  the  EOCIC  and  largely  based  on  regional 
stakeholder consultation.  

Finally, the analysis of the internationalisation trends and global value chain changes is 
complemented  by  a  comparative  analysis  of  the  industrial  transformation  trends 
observed in the EU with a sample of third countries outside the EU (USA, Canada, Japan, 
Singapore and the Republic of Korea). The objective is to assess the positioning of the 
European  emerging  industries  in  the  global  landscape  and,  by  doing  so,  to  identify 
opportunities  (or  competition  threats)  that  could  have  an  impact  on  cross-sectoral 
industrial development within the EU. This analysis is mainly qualitative and relies on a 
review  of  the  briefing  reports  on  international  partner  countries  provided  by  the 
European Cluster Collaboration Platform (ECCP) between 2017 and 2018. 

24 https://ec.europa.eu/growth/industry/policy/cluster/observatory_en 

36 | P a g e  

 
 
 
 
                                              
5 Analysis of the role of clusters in 

industrial transformation 

Industrial transformation and the development of emerging industries can represent 
both opportunities and key challenges for enterprises, since the nature of innovations 
and  how  they  are  emerging  is  continuously  changing  and  require  learning  and 
adaptation capabilities to reap the benefits arising from the ongoing transformation 
trends.  Cluster  organisations  can  play  a  decisive  role  to  facilitate  entrepreneurship, 
cross-sectoral collaboration and the development of new industrial value chains. If the 
identification  and  analysis  of  global  megatrends  focus  on  the  external  factors  that 
could  affect  industrial  modernisation,  this  last  analytical  block  targets  the  internal 
influencing  factors,  looking  particularly  at  the  role  of  clusters  in  favouring  or 
accompanying cross-sectoral industrial modernisation in the EU.  

The  analysis  of  the  role  of  clusters  in  industrial  transformation  aims  to  address  the 
following questions:  

  What  types  of  services  are  provided  by  clusters  to  facilitate  cross-sectoral 

transformation? 

  What are the differences in the amount and type of support provided by cluster 

organisations in the ten emerging industries? 

  With  specific  reference  to  internationalisation,  to  what  extent  do  cluster 
organisations contribute to improve and/or maintain the internationalisation of 
their  cluster  participants?  and  What  is  the  relationship  between  the  strategic 
importance  of 
for 
internationalisation?  

internationalisation 

available 

services 

and 

the 

  Is  there  a  demand  for  new  forms  of  support  services  which  better  fit  with  the 

opportunities and challenges posed by future industrial developments? 

These questions are addressed by resorting to multiple data sources and information 
produced  by  the  European  Observatory  of  Clusters  and 
Industrial  Change, 
complemented with external data sources. 

More specifically, the analysis builds on available evidence on the many examples of 
cluster  support  services  that  emerge  from  the  literature  review.  The  previous 

37 | P a g e  

 
 
Observatory25 produced the report “Cluster Collaboration and Business Support Tools 
to Facilitate Entrepreneurship, Cross-sectoral Collaboration and Growth” (September 
2014), 26  which  offers  a  comparative  analysis  of  benchmarking  data  of  cluster 
organisations from both traditional industries and the emerging industries. It provides 
many  examples  of  cluster  services,  such  as  marketing  intelligence,  matchmaking 
activities,  services  of  cross-sectoral 
innovation,  technology  transfer,  strategic 
networking.  The  report  pays  particular  attention  to  clusters  operating  in  emerging 
industries where traditional cluster services alone may not be sufficient anymore and 
new  cluster  services  are  needed  to  support  small  and  medium-sized  enterprises 
operating in these changing industries. Other guides highlight suitable instruments in 
specific  service  areas,  like  “The  Smart  Guide  to  Service  Innovation  –  How  to  better 
capitalise  on  service  innovation  for  regional  structural  change  and  industrial 
modernisation”  (European  Commission,  2012) 27  and  the  handbook  “To  Do: 
Development of cultural and creative industries in practices” (Alexanderson Institute, 
2013). These and other sources are reviewed with the purpose of gathering information 
and concrete examples about cluster policies at  the national and regional level. The 
focus is on services targeted to support cross-sectoral industrial modernisation.  

Furthermore, the findings of the work under the EOCIC Work Package 428 (Support 
for mutual cluster policy learning, policy mapping and transnational cooperation) are 
used to gather additional and more recent information on the contribution of clusters 
policies  to  industrial  modernisation.  Particularly  the  findings  of  the  online  survey  to 
national and regional policy makers (Task 4.1) are analysed to derive more insights on 
the instruments in place for addressing cross-sectoral industrial transformation trends. 
This survey traces the development of cluster policies in Europe during the last years. 
Questions included in the Task 4.1 survey which are of interest for the Trends report 
refer to: 

  The  available  programmes  supporting  internationalisation  activities  which  are 
focused  on  more  than  one  sectors  and  which  address  clusters  in  emerging 
technologies of industries. 

25 https://ec.europa.eu/growth/industry/policy/cluster/observatory/previous-results_en 

26 https://www.clustercollaboration.eu/eu-initiatives/reports/cluster-collaboration-and-
business-support-tools, see: 
https://ec.europa.eu/growth/industry/policy/cluster/observatory/previous-results_en 

27 http://ec.europa.eu/DocsRoom/documents/3955/attachments/1/translations/en/renditions/pdf  

28 https://ec.europa.eu/growth/industry/policy/cluster/observatory_en 

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  Descriptive information on the selected programmes, such as the country where 
the  programme  is  in  place,  the  programme’s  duration,  the  type  of  support 
provided and others; 

  Whether 

the  programme 

includes  measures 

to  support  cross-sectoral 

collaboration.  

Data collected by the ESCA to quality audit and benchmark European clusters are also 
examined.  Data  are  available  for  hundreds  of  cluster  organisations  and  rely  on  the 
cluster  managers’  own  assessment  on  a  discrete  scale  (from  0  to  4).  Only  data 
belonging  to  the  selected  ten  emerging  industries  are  considered.  While  some 
indicators are relevant to address the type and level of internationalisation of clusters, 
as  discussed  in  the  previous  analytical  step,  now  the  focus  is  put  on  the  following 
indicators: 

  The importance of internationalisation in the strategy of the cluster 
  Activities and services regarding internationalisation provided within the cluster 
(e.g. 
initiating 
international R&D cooperation and projects or opening an international business) 
  Degree  of  positive  effects  of  the  activities  within  the  cluster  regarding  the 

trans-national  cooperation, 

initiation  and 

facilitation  of 

internationalisation of the cluster participants. 

The  analysis  focuses  on  the  most  recent  data  with  a  view  of  providing  an  updated 
picture of the current trends regarding the business support services offered by cluster 
organisations to support cluster participants.  

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6 Synthesis 

The following table summarises the methodological tools that  contribute to each of 
the  building  blocks  of  the  analytical  framework.  A  key  point  is  that  most  of  the 
methodological tools mobilised provide insights on more than one analytical blocks. 
The  combination  of  different  tools  allows  ensuring  a  far-reaching,  up-to-date  and 
unbiased understanding of the subject of study. 

Table 5 – Contribution of the methodological tools to the Trends Report analysis  

Methodological 
tool 

Analytical blocks 

Identification and 
analysis of 
megatrends 

Analysis of cross-
sectoral dynamics of 
industrial 
transformation 

Analysis of 
transformation 
trends within and 
beyond Europe 

Analysis of the 
role of clusters 

Literature and 
documentary 
review 

Delphi 
consultation  

Quantitative 
data analysis 

Stakeholder 
consultation 
(surveys, 
interviews) 

√ 

√ 

√ 

√ 

√ 

√ 

√ 

√ 

√ 

√ 

Source: EOCIC 

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Zawadzki,  P.,  Żywicki,  K.  (2016).  Smart  Product  Design  and  Production  Control  for 
Effective  Mass  Customization  in  the  Industry  4.0  Concept.  Manag.  Prod.  Eng.  Rev.  7, 
105–112. https://doi.org/10.1515/mper-2016-0030 

52 | P a g e  

 
 
 
List of experts consulted 

Bulgarian Academy of Sciences 
JRC 

Affiliation 

Position 
Group 1 – Digital and new production technologies 
Sociologist 
Senior researcher in industrial and 
chemical engineering 
Researcher in Economics and 
Industrial management 
Consultant 
Consultant 
Consultant 
Founder 
Project manager 
Professor of Philosophy of Science 

Deloitte BE 
Deloitte BE 
Deloitte BE 
N-Able 
Fraunhofer 
University of Naples Frederico II 

FRI/University of Florence 

Group 2 – Globalisation and new protectionism 

Associate Professor of Economics 
Senior Analyst in International 
Economics 
Professor of Sociology 
Trade policy analyst 
Professor of Economics 
Fellow Global Economy and 
Development (International 
Relations) 
Senior Economist 
Professor of Sociology 

ESSEC Asia Pacific 
Elcano Royal Institute 

Sciences Po / UCLA 
Sciences Po 
MIT 
Brookings/Oxford 

OECD 
Hebrew University of Jerusalem 

Group 3 – Cities and transportation 

Director transport studies unit 
Advisor - Innovation and Foresight  OECD-ITF 
Consultant 
Researcher 
Cities & Urban Development Expert, 
Urban Development Lead 
Political scientist 
Senior energy and transport analyst 
Consultant 

Sciences Po 
IEA 
SPI 

University of Oxford 

Deloitte PT 
Fraunhofer 
World Economic Forum 

Name 

1.  Vassil Kirov 
2.  Hector Hernandez 

Guevara 

3.  Simona Ciappei 

4.  Tim Paridaens 
5.  Els Renders 
6.  Athina Stantzos 
7.  Pierre Padilla 
8.  Djerdj Horvat  
9.  Guglielmo Tamburrini 

1.  Jamus Lim 
2.  Federico Steinberg 

3.  Michael Storper 
4.  Sébastien Miroudot 
5.  David Atkin 
6.  Geoffrey Gertz 

7.  Koen De Backer 
8.  Gili Drori 

1.  Tim Schwanen 
2.  Philippe Crist 
3.  Miguel Eiras Antunes 
4.  Jonathan Köhler 
5.  Alice Charles 

6.  Charlotte Halpern 
7.  Pierpaolo Cazzola 
8.  Leonel Ferreira 

Country  

BG 
ES 

IT 

BE 
BE 
EL 
FR 
DE 
IT 

SG 
ES 

US 
FR 
US 
CA 

BE 
IL 

GB 
FR 
PT 
DE 
IE 

FR 
IT 
PT 

53 | P a g e  

 
 
Name 

Position 

Affiliation 

Country  

Group 4 – Green and circular economy 

1.  Lucas Porsch 
2.  Eleanor Drabik 

Associate Director 
Researcher 

3.  Emmanuelle 

Director sustainable development 

Neyroumande 
4.  Jean-Alain Héraud 

Economist 

5.  Carsten Gandenberger 
6.  Laurent Bontoux 
7.  Benoit Tinetti 
8.  Jutta Niederste-
Hollenberg 

Project manager 
Foresight specialist (environmental) 
Director sustainability 
Environmental engineer, project 
manager 

VVA 
University of Sussex / Centre for 
Research into Energy Demand 
Solutions 
Deloitte FR 

Strasbourg Conseil / University of 
Strasbourg 
Fraunhofer 
JRC/European Commission 
Deloitte FR 
Fraunhofer 

1.  Kerstin Cuhls 
2.  Alexia Fürnkranz-

Prskawetz 

Group 5 – Demographic shifts 

Scientific project manager 
Economist of population 

Fraunhofer 
OAW 

DE 
GB 

FR 

FR 

DE 
FR 
FR 
DE 

DE 
AT 

3.  Stuart Gietel-Basten 

Associate Professor of Demography  Hong Kong University of Science 

GB 

and Technology 

4.  Ronald Demos Lee 
5.  J.A.A. de Beer 

1.  Agne Vezbergiene 
2.  Frank Piller 
3.  Alessandra Tracogna 

NIDI 

Professor Emeritus of Demography  UC Berkley 
Theme leader population ageing 
and longevity 
Group 6 – Product customisation and servitisation 
Consultant 
Head of the Institute 
Director of Industry and Market 
research  

Deloitte BE 
RWTH Aachen university 
CSIL Centre for Industrial Studies 

US 
NL 

LT 
DE 
IT 

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Emerging industries: NACE 

definition  

Table 6 – Advanced Packaging: NACE definition 

NACE Description 
Manufacture of pulp 
Manufacture of paper and paperboard 
Manufacture of corrugated paper and paperboard and of containers of paper and paperboard 
Manufacture of household and sanitary goods and of toilet requisites 
Manufacture of paper stationery 
Manufacture of wallpaper 
Manufacture of other articles of paper and paperboard 
Manufacture of plastic plates, sheets, tubes and profiles 
Manufacture of plastic packing goods 
Manufacture of other plastic products 
Manufacture of tubes, pipes, hollow profiles and related fittings, of steel 
Treatment and coating of metals 
Manufacture of tools 
Manufacture of other fabricated metal products n.e.c. 
Manufacture of other special-purpose machinery n.e.c. 
Manufacture of other parts and accessories for motor vehicles 
Manufacture of office and shop furniture 
Wholesale of other intermediate products 

Table 7 – Biopharmaceuticals: NACE definition 

NACE Description 
Distilling, rectifying and blending of spirits 
Manufacture of household and sanitary goods and of toilet requisites 
Manufacture of other inorganic basic chemicals 
Manufacture of other organic basic chemicals 
Manufacture of paints, varnishes and similar coatings, printing ink and mastics 
Manufacture of soap and detergents, cleaning and polishing preparations 
Manufacture of other chemical products n.e.c. 
Manufacture of basic pharmaceutical products 
Manufacture of pharmaceutical preparations 
Wholesale of pharmaceutical goods 
Research and experimental development on biotechnology 
Other research and experimental development on natural sciences and engineering 

NACE 
17.11 
17.12 
17.21 
17.22 
17.23 
17.24 
17.29 
22.21 
22.22 
22.29 
24.20 
25.61 
25.73 
25.99 
28.99 
29.32 
31.01 
46.76 

NACE 
11.01 
17.22 
20.13 
20.14 
20.30 
20.41 
20.59 
21.10 
21.20 
46.46 
72.11 
72.19 

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Table 8 – Blue Growth Industries: NACE definition 

NACE Description 
Marine fishing 
Freshwater fishing 
Support activities for petroleum and natural gas extraction 
Processing and preserving of fish, crustaceans and molluscs 
Manufacture of other rubber products 
Manufacture of other fabricated metal products n.e.c. 
Manufacture of engines and turbines, except aircraft, vehicle and cycle engines 
Manufacture of lifting and handling equipment 
Building of ships and floating structures 
Building of pleasure and sporting boats 
Repair and maintenance of ships and boats 
Production of electricity 
Transmission of electricity 
Water collection, treatment and supply 
Construction of water projects 
Agents involved in the sale of machinery, industrial equipment, ships and aircraft 
Freight transport by road 
Sea and coastal passenger water transport 
Sea and coastal freight water transport 
Inland passenger water transport 
Inland freight water transport 
Warehousing and storage 
Service activities incidental to water transportation 
Service activities incidental to air transportation 
Cargo handling 
Other transportation support activities 
Engineering activities and related technical consultancy 
Technical testing and analysis 
Other research and experimental development on natural sciences and engineering 
Advertising agencies 
Renting and leasing of construction and civil engineering machinery and equipment 
Renting and leasing of water transport equipment 
Travel agency activities 

NACE 
03.11 
03.12 
09.10 
10.20 
22.19 
25.99 
28.11 
28.22 
30.11 
30.12 
33.15 
35.11 
35.12 
36.00 
42.91 
46.14 
49.41 
50.10 
50.20 
50.30 
50.40 
52.10 
52.22 
52.23 
52.24 
52.29 
71.12 
71.20 
72.19 
73.11 
77.32 
77.34 
79.11 

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Table 9 – Creative Industries: NACE definition 

NACE Description 
Reproduction of recorded media 
Taxi operation 
Book publishing 
Publishing of directories and mailing lists 
Publishing of journals and periodicals 
Other publishing activities 
Motion picture, video and television programme production activities 
Motion picture, video and television programme post-production activities 
Motion picture, video and television programme distribution activities 
Sound recording and music publishing activities 
Computer programming activities 
Computer consultancy activities 
Computer facilities management activities 
Other information technology and computer service activities 
Data processing, hosting and related activities 
Web portals 
News agency activities 
Other information service activities n.e.c. 
Activities of holding companies 
Activities of head offices 
Public relations and communication activities 
Business and other management consultancy activities 
Architectural activities 
Engineering activities and related technical consultancy 
Technical testing and analysis 
Advertising agencies 
Media representation 
Market research and public opinion polling 
Specialised design activities 
Translation and interpretation activities 
Other professional, scientific and technical activities n.e.c. 
Renting and leasing of trucks 
Leasing of intellectual property and similar products, except copyrighted works 
Activities of employment placement agencies 
Other human resources provision 
Combined facilities support activities 
Activities of call centres 
Organisation of conventions and trade shows 
Library and archives activities 

NACE 
18.20 
49.32 
58.11 
58.12 
58.14 
58.19 
59.11 
59.12 
59.13 
59.20 
62.01 
62.02 
62.03 
62.09 
63.11 
63.12 
63.91 
63.99 
64.20 
70.10 
70.21 
70.22 
71.11 
71.12 
71.20 
73.11 
73.12 
73.20 
74.10 
74.30 
74.90 
77.12 
77.40 
78.10 
78.30 
81.10 
82.20 
82.30 
91.01 

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Table 10 – Digital Industries: NACE definition 

NACE Description 
Manufacture of other plastic products 
Treatment and coating of metals 
Manufacture of tools 
Manufacture of electronic components 
Manufacture of loaded electronic boards 
Manufacture of computers and peripheral equipment 
Manufacture of communication equipment 
Manufacture of consumer electronics 
Manufacture of instruments and appliances for measuring, testing and navigation 
Manufacture of watches and clocks 
Manufacture of optical instruments and photographic equipment 
Manufacture of magnetic and optical media 
Manufacture of electricity distribution and control apparatus 
Manufacture of other electrical equipment 
Manufacture of power-driven hand tools 
Manufacture of other general-purpose machinery n.e.c. 
Manufacture of other special-purpose machinery n.e.c. 
Manufacture of medical and dental instruments and supplies 
Wholesale of electrical household appliances 
Wholesale of computers, computer peripheral equipment and software 
Wholesale of electronic and telecommunications equipment and parts 
Wholesale of other office machinery and equipment 
Wholesale of other machinery and equipment 
Book publishing 
Publishing of computer games 
Other software publishing 
Wireless telecommunications activities 
Satellite telecommunications activities 
Other telecommunications activities 
Computer programming activities 
Computer consultancy activities 
Other information technology and computer service activities 
Public relations and communication activities 
Market research and public opinion polling 

NACE 
22.29 
25.61 
25.73 
26.11 
26.12 
26.20 
26.30 
26.40 
26.51 
26.52 
26.70 
26.80 
27.12 
27.90 
28.24 
28.29 
28.99 
32.50 
46.43 
46.51 
46.52 
46.66 
46.69 
58.11 
58.21 
58.29 
61.20 
61.30 
61.90 
62.01 
62.02 
62.09 
70.21 
73.20 

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Table 11 – Environmental Industries: NACE definition 

NACE Description 
Extraction of natural gas 
Support activities for petroleum and natural gas extraction 
Manufacture of veneer sheets and wood-based panels 
Manufacture of other products of wood manufacture of articles of cork, straw and plaiting materials 
Manufacture of household and sanitary goods and of toilet requisites 
Manufacture of other organic basic chemicals 
Manufacture of fertilisers and nitrogen compounds 
Manufacture of plastics in primary forms 
Manufacture of other chemical products n.e.c. 
Manufacture of other plastic products 
Manufacture of other ceramic products 
Manufacture of cement 
Manufacture of basic iron and steel and of ferro-alloys 
Manufacture of central heating radiators and boilers 
Manufacture of steam generators, except central heating hot water boilers 
Manufacture of other fabricated metal products n.e.c. 
Manufacture of instruments and appliances for measuring, testing and navigation 
Manufacture of engines and turbines, except aircraft, vehicle and cycle engines 
Manufacture of other general-purpose machinery n.e.c. 
Manufacture of other special-purpose machinery n.e.c. 
Production of electricity 
Transmission of electricity 
Water collection, treatment and supply 
Collection of hazardous waste 
Treatment and disposal of hazardous waste 
Dismantling of wrecks 
Recovery of sorted materials 
Wholesale of grain, unmanufactured tobacco, seeds and animal feeds 
Wholesale of flowers and plants 
Transport via pipeline 
Service activities incidental to water transportation 
Other transportation support activities 
Engineering activities and related technical consultancy 
Research and experimental development on biotechnology 
Other research and experimental development on natural sciences and engineering 

NACE 
06.20 
09.10 
16.21 
16.29 
17.22 
20.14 
20.15 
20.16 
20.59 
22.29 
23.49 
23.51 
24.10 
25.21 
25.30 
25.99 
26.51 
28.11 
28.29 
28.99 
35.11 
35.12 
36.00 
38.12 
38.22 
38.31 
38.32 
46.21 
46.22 
49.50 
52.22 
52.29 
71.12 
72.11 
72.19 

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Table 12 – Experience Industries: NACE definition 

NACE Description 
Hunting, trapping and related service activities 
Agents specialised in the sale of other particular products 
Wholesale of clothing and footwear 
Wholesale of other household goods 
Retail sale via mail order houses or via Internet 
Other passenger land transport n.e.c. 
Inland passenger water transport 
Inland freight water transport 
Service activities incidental to water transportation 
Service activities incidental to air transportation 
Hotels and similar accommodation 
Holiday and other short-stay accommodation 
Camping grounds, recreational vehicle parks and trailer parks 
Other accommodation 
Other software publishing 
Computer programming activities 
Other information technology and computer service activities 
Web portals 
Public relations and communication activities 
Business and other management consultancy activities 
Renting and leasing of recreational and sports goods 
Travel agency activities 
Tour operator activities 
Other reservation service and related activities 
Organisation of conventions and trade shows 
Performing arts 
Support activities to performing arts 
Artistic creation 
Operation of arts facilities 
Museums activities 
Operation of historical sites and buildings and similar visitor attractions 
Botanical and zoological gardens and nature reserves activities 
Gambling and betting activities 
Operation of sports facilities 
Activities of sport clubs 
Other sports activities 
Activities of amusement parks and theme parks 
Other amusement and recreation activities 

NACE 
01.70 
46.18 
46.42 
46.49 
47.91 
49.39 
50.30 
50.40 
52.22 
52.23 
55.10 
55.20 
55.30 
55.90 
58.29 
62.01 
62.09 
63.12 
70.21 
70.22 
77.21 
79.11 
79.12 
79.90 
82.30 
90.01 
90.02 
90.03 
90.04 
91.02 
91.03 
91.04 
92.00 
93.11 
93.12 
93.19 
93.21 
93.29 

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Table 13 – Logistical Services: NACE definition 

NACE Description 
Repair and maintenance of aircraft and spacecraft 
Taxi operation 
Other passenger land transport n.e.c. 
Freight transport by road 
Passenger air transport 
Freight air transport 
Space transport 
Service activities incidental to land transportation 
Service activities incidental to air transportation 
Cargo handling 
Other transportation support activities 
Satellite telecommunications activities 

Table 14 – Medical Devices: NACE definition 

NACE Description 
Manufacture of bricks, tiles and construction clay 
Manufacture of weapons and ammunition 
Manufacture of tools 
Manufacture of electronic components 
Manufacture of loaded electronic boards 
Manufacture of consumer electronics 
Manufacture of instruments and appliances for measuring, testing and navigation 
Manufacture of watches and clocks 
Manufacture of irradiation, electromedical equipment 
Manufacture of optical instruments and photographic equipment 
Manufacture of electricity distribution and control apparatus 
Manufacture of fibre optic cables 
Manufacture of other electronic and electric wires and cables 
Manufacture of wiring devices 
Manufacture of electric domestic appliances 
Manufacture of other electrical equipment 
Manufacture of fluid power equipment 
Manufacture of other pumps and compressors 
Manufacture of non-domestic cooling and ventilation equipment 
Manufacture of other general-purpose machinery n.e.c. 
Manufacture of machinery for paper and paperboard production 
Manufacture of other special-purpose machinery n.e.c. 
Manufacture of medical and dental instruments and supplies 
Wholesale of pharmaceutical goods 
Computer facilities management activities 

NACE 
33.16 
49.32 
49.39 
49.41 
51.10 
51.21 
51.22 
52.21 
52.23 
52.24 
52.29 
61.30 

NACE 
23.32 
25.40 
25.73 
26.11 
26.12 
26.40 
26.51 
26.52 
26.60 
26.70 
27.12 
27.31 
27.32 
27.33 
27.51 
27.90 
28.12 
28.13 
28.25 
28.29 
28.95 
28.99 
32.50 
46.46 
62.03 

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Table 15 – Mobility Technologies: NACE definition 

NACE Description 
Manufacture of plastic plates, sheets, tubes and profiles 
Manufacture of other plastic products 
Manufacture of basic iron and steel and of ferro-alloys 
Casting of light metals 
Casting of other non-ferrous metals 
Manufacture of steam generators, except central heating hot water boilers 
Forging, pressing, stamping and roll-forming of metal powder metallurgy 
Treatment and coating of metals 
Machining 
Manufacture of tools 
Manufacture of fasteners and screw machine products 
Manufacture of other fabricated metal products n.e.c. 
Manufacture of electronic components 
Manufacture of electric motors, generators and transformers 
Manufacture of electricity distribution and control apparatus 
Manufacture of other electronic and electric wires and cables 
Manufacture of wiring devices 
Manufacture of electric domestic appliances 
Manufacture of other electrical equipment 
Manufacture of engines and turbines, except aircraft, vehicle and cycle engines 
Manufacture of fluid power equipment 
Manufacture of other pumps and compressors 
Manufacture of bearings, gears, gearing and driving elements 
Manufacture of lifting and handling equipment 
Manufacture of power-driven hand tools 
Manufacture of non-domestic cooling and ventilation equipment 
Manufacture of other general-purpose machinery n.e.c. 
Manufacture of agricultural and forestry machinery 
Manufacture of metal forming machinery 
Manufacture of other machine tools 
Manufacture of machinery for mining, quarrying and construction 
Manufacture of machinery for textile, apparel and leather production 
Manufacture of machinery for paper and paperboard production 
Manufacture of plastics and rubber machinery 
Manufacture of other special-purpose machinery n.e.c. 
Manufacture of motor vehicles 
Manufacture of bodies (coachwork) for motor vehicles manufacture of trailers and semi-trailers 
Manufacture of electrical and electronic equipment for motor vehicles 
Manufacture of other parts and accessories for motor vehicles 
Manufacture of railway locomotives and rolling stock 
Manufacture of air and spacecraft and related machinery 
Manufacture of military fighting vehicles 
Manufacture of bicycles and invalid carriages 
Wholesale of other machinery and equipment 

NACE 
22.21 
22.29 
24.10 
24.53 
24.54 
25.30 
25.50 
25.61 
25.62 
25.73 
25.94 
25.99 
26.11 
27.11 
27.12 
27.32 
27.33 
27.51 
27.90 
28.11 
28.12 
28.13 
28.15 
28.22 
28.24 
28.25 
28.29 
28.30 
28.41 
28.49 
28.92 
28.94 
28.95 
28.96 
28.99 
29.10 
29.20 
29.31 
29.32 
30.20 
30.30 
30.40 
30.92 
46.69 

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European Observatory for Clusters and 

Industrial Change 

Internal  Market, 

The European Observatory for Clusters and Industrial Change (#EOCIC) is an initiative of the European 
Industry,  Entrepreneurship  and  SMEs  Directorate-General.  The 
Commission’s 
Observatory provides a single access point for statistical information, analysis and mapping of clusters 
and cluster policy in Europe, aimed at European, national, regional and local policy-makers, as well as 
cluster managers and representatives of SME intermediaries.  

The  aim  of  the  Observatory  is  to  help  Europe's  regions  and 
countries design better and more evidence‐based cluster policies 
and  initiatives  that  help  countries  participating  in  the  COSME 
programme to:  

 develop  world‐class  clusters  with  competitive  industrial  value 
chains that cut across sectors;  
 support Industrial modernisation; 
 foster  Entrepreneurship  in  emerging  industries  with  growth 
potential; 
 improve  SMEs'  access  to  clusters  and  internationalisation 
activities; and 
 enable  more 
investments 
strategies. 

inter‐regional  collaboration  and 
implementation  of  smart  specialisation 

strategic 

in  the 

In  order  to  address  these  goals,  the  Observatory  provides  an 
Europe-wide comparative cluster mapping with sectoral and cross-
sectoral  statistical  analysis  of  the  geographical  concentration  of 
economic  activities  and  performance,  made  available  on  the 
website of the European Cluster Collaboration Platform (ECCP)29. 
The Observatory provides the following services:  

  Bi-annual "European Panorama of Clusters and Industrial Change" that analyses cluster 
strengths and development trends across 51 cluster sectors and 10 emerging industries, and 
investigates the linkages between clusters and industrial change, entrepreneurship, growth, 
innovation, internationalisation and economic development; 

 

"Cluster  and  Industrial  Transformation  Trends  Report"  which 
transformation of clusters, new specialisation patterns and emerging industries; 

investigates  the 

  Cluster policy mapping in European countries and regions as well as in selected non-

European countries; 

29 https://www.clustercollaboration.eu/ 

63 | P a g e  

 
 
                                              
 

"Regional  Eco-system  Scoreboard  for  Clusters  and  Industrial  Change"  that  identifies 
and  captures  favourable  framework  conditions  for 
innovation, 
entrepreneurship and cluster development; 

industrial  change, 

  Updated European Service Innovation Scoreboard30, that provides scorecards on service 

innovation for European regions; 

 

"European  Stress  Test  for  Cluster  Policy",  including  a  self-assessment  tool  targeted  at 
cross-sectoral  collaboration,  innovation  and  entrepreneurships  with  a  view  to  boosting 
industrial change; 

  Customised  advisory  support  services  to  twelve  selected  model  demonstrator  regions, 
including expert analysis, regional survey and benchmarking report, peer-review meeting, 
and policy briefings in support of industrial modernisation; 

  Advisory support service to European Strategic Cluster Partnerships, in order to support 
networking between the partnerships and to support exchanges of successful practices for 
cross-regional collaborations and joint innovation investments; 

  Smart  Guides  for  cluster  policy  monitoring  and  evaluation,  and  for  entrepreneurship 

support through clusters that provide guidance for policy-makers; and 

  Brings  together  Europe’s  cluster  policy-makers  and  stakeholders  at  four  European 
Cluster Policy Forum events, the EU Cluster Weeks, and at the European Cluster Conference 
In order to facilitate high-level cluster policy dialogues, exchanges with experts and mutual 
cluster policy learning. Four European Cluster Policy Forums took place in February, April, 
November 2018 and March 2019 in Brussels. The European Cluster Conference took place 
from 14 to 16 May 2019 in Bucharest (Romania) with support of the Romanian Presidency 
to the EU. 

  Online  presentations  and  publications,  discussion  papers,  newsletters,  videos  and  further 
promotional material accompany and support information exchanges and policy learning 
on cluster development, cluster policies and industrial change. 

More information about the European Observatory for Clusters  and Industrial Change is available at: 
https://www.clustercollaboration.eu/eu-initiatives/european-cluster-observatory

30 Previous versions for 2014 and 2015 were developed by the European Service Innovation Centre (ESIC), see 
http://ec.europa.eu/growth/tools-databases/esic/index_en.htm 

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