A policy oriented analytical approach to map the digital ecosystem (DGTES).
Date: 2022-11-28
Institutions: Joint Research Centre
Subjects: EU industrial policy, EU initiative, EU policy, artificial intelligence, competitiveness, data processing, database, digital transformation, research report

A policy oriented analytical approach  
to map the digital ecosystem (DGTES) 

Methodology and 
overview 

ISSN 1831-9424 

EUR 31319 EN 

 
 
 
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Contact information  
Name: Giuditta De Prato  
Address: Edificio Expo, C/ Inca Garcilaso 3, Sevilla 41092, Spain 
Email: Giuditta.DE-PRATO@ec.europa.eu 

EU Science Hub 
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JRC130799 

EUR 31319 EN 

PDF 

ISBN 978-92-76-59139-9 

ISSN 1831-9424 

doi:10.2760/37629  KJ-NA-31-319-EN-N 

Luxembourg: Publications Office of the European Union, 2022  

© European Union, 2022  

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How to cite this report: Calza, E., Dalla Benetta, A., Kostić, U., Mitton, I., Vázquez-Prada Baillet, M., Carenini, M., Cira, P.P., De Prato, G., Righi, 
R., Papazoglou, M., López Cobo, M., Cardona, M., A policy oriented analytical approach to map the digital ecosystem (DGTES), Publications 
Office of the European Union, Luxembourg, 2022, doi:10.2760/37629, JRC130799. 

 
 
 
 
 
 
 
 
 
 
 
 
 
      
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents  

Abstract ....................................................................................................................................................................................................................................................................... 1 

Foreword ..................................................................................................................................................................................................................................................................... 2 

Acknowledgements .......................................................................................................................................................................................................................................... 3 

Executive summary .......................................................................................................................................................................................................................................... 5 

1 

Introduction..................................................................................................................................................................................................................................................... 9 

2  TES: grasping the complexity of dynamic Techno-Economic ecoSystems ..................................................................................... 12 

2.1  A new instrument to map Techno-Economic ecoSystems ................................................................................................................ 12 

2.2  Beyond the scope of the TES analytical approach ................................................................................................................................... 15 

3  DGTES: applying the TES methodological approach to the digital ecosystem............................................................................ 17 

3.1  The technological boundaries of the digital ecosystem: digital areas .................................................................................. 17 

3.1.1  Why the notion of digital area................................................................................................................................................................... 17 

3.1.2  What is a digital area ......................................................................................................................................................................................... 18 

3.1.3 

How digital areas were identified .......................................................................................................................................................... 18 

3.1.4 

How digital areas serve the implementation of DGTES .................................................................................................... 20 

3.2  The DGTES ecosystem database ............................................................................................................................................................................... 20 

3.2.1 

Detecting activities leaving a ‘digital footprint’ ........................................................................................................................ 21 

3.2.2 

Identifying the players shaping the digital ecosystem ...................................................................................................... 23 

3.2.3 

Generating the graph database to map the digital ecosystem ................................................................................. 24 

4  DGTES at work: mapping and navigating through the digital ecosystem ....................................................................................... 26 

4.1  An itinerary to explore the digital ecosystem ............................................................................................................................................... 26 

4.2  DGTES indicators and metrics ...................................................................................................................................................................................... 26 

4.2.1 

An overview of the digital global landscape ................................................................................................................................ 27 

4.2.2 

A collaborative ecosystem ............................................................................................................................................................................ 30 

4.3  Further applications and extensions of the DGTES ................................................................................................................................. 32 

5  Conclusions: DGTES for a digital, sustainable, resilient and globally competitive EU economy ................................ 35 

5.1  Fostering digital transformation in an changing global landscape: a renewed policy challenge for the 
EU  35 

5.2  A policy-oriented analytical instrument to steer digital transformation ............................................................................ 36 

References ............................................................................................................................................................................................................................................................. 40 

List of abbreviations and definitions ........................................................................................................................................................................................... 42 

List of boxes......................................................................................................................................................................................................................................................... 43 

List of figures ..................................................................................................................................................................................................................................................... 44 

List of tables ........................................................................................................................................................................................................................................................ 45 

Annexes .................................................................................................................................................................................................................................................................... 46 

Annex 1. Identification of digital areas ............................................................................................................................................................................. 46 

i 

 
 
ii 

 
 
 
 
 
 
 
 
 
 
 
 
Abstract  

This report describes the Digital Techno-Economic ecoSystem (DGTES), which is the application of the Techno-
Economic ecoSystem (TES) analytical approach to the analysis of the digital ecosystem. It explains the steps 
leading to the  construction of the  DGTES  digital ecosystem graph  dataset and  highlights the originality and 
the  advantages  of  this  approach.  The  report  also  offers  an  overview  of  a  set  of  indicators  that  can  be 
generated from the DGTES digital ecosystem dataset, in order to provide a global and comprehensive analysis 
of the digital ecosystem while keeping the policy perspective in mind.  

This report reflects on how DGTES can be used to formulate policy-relevant recommendations. The results of 
DGTES can provide novel insights for a better discussion within the framework of main European Union’s (EU) 
policy  initiatives  aiming  at  strengthening  Europe’s  competitiveness  and  leadership,  and  inform  policies 
fostering  the  development  of  the  technological  and  industrial  capabilities  needed  for  a  vibrant  digital 
ecosystem in Europe.  

The TES approach had already been applied to other techno-economic segments that are rapidly evolving and 
are  expected  to  play  a  key  role  in  the  digital  transformation,  such  as  photonics,  earth  observations 
technologies, and artificial intelligence (AI) 1. DGTES is the first example of a more comprehensive application 
of the TES analytical approach to the study of an industrial ecosystem. 

(1)   See the JRC Technical Reports ‘The AI Techno-Economic Segment Analysis’ (De Prato et al. 2019), ‘The techno-economic segment of 
Earth  observation’  (Pogorzelska  et  al.  2019),  and  ‘Unveiling  Latent  Relations  in  the  Photonics  Techno-Economic  Complex  System’ 
(Samoili et al. 2019). 

1 

 
                                                        
 
Foreword  

Digital  transformation  is  one  of  the  key  phenomena  our  societies  are 
experiencing.  Digital  technologies  are  deeply  transforming  the  way  we 
produce,  consume,  learn,  work  and  enjoy  life,  having  tremendous  impact 
on  our  societies  and  economies.  The  way  we  approach  this  crucial 
transformation  is  key  to  ensure  we  will  make  it  a  successful  one:  it  will 
have  to  elevate  us,  individually  and  collectively,  by  enabling  a  human-
cantered, sustainable and more prosperous future. 

There are  plenty of aspects  surrounding digital transformation that need 
to  be  taken  into  account:  social  consequences,  economic  effects, 
environmental 
is  a 
multifaceted transition that,  in order to be  deeply  understood,  requires a 
holistic  approach.  In  particular,  it  is  extremely  important  to  be  able  to 
systematically identify which are the relevant players driving this phenomenon, how they interact and engage, 
where  their  activities  take  place  and  around  which  technological  domains.  This  is  what  we  name  a  digital 
ecosystem. 

industrial  changes  among  others. 

impact, 

It 

The concept of digital ecosystem lies at the core of digital transformation. Understanding it, is therefore key 
for  grasping  and  steering  the  digital  transformation,  and  has  various  policy  implications  –  including  a  new 
European industrial policy, European open strategic autonomy, the achievement of the Digital Decade goals or 
the identification of European champions in emerging digital technologies. 

This  report  presents  the  application  of  the  Techno-Economic  ecoSystems  (TES)  methodology  to  the 
identification of the digital ecosystem, focusing on the methodological aspects. It will be the first report of a 
series of two, the latter presenting the details and results of the analysis. This work is part of our effort, at 
JRC,  to  identify  and  capture,  in  a  forward  looking  and  policy-relevant  way,  the  effects  and  opportunities 
brought by digital transformation. 

Director, Growth and Innovation Directorate, European Commission Joint Research Centre (JRC) 

Mikel Landabaso Alvarez  

2 

 
 
Acknowledgements  

This  report  has  been  prepared  within  the  framework  of  the  project  IDITES  (The  Impact  of  Digital 
Transformation  on  the  Economy  and  Society)  implemented  by  the  Digital  Economy  Unit  of  the  European 
Commission’s  (EC)  Joint  Research  Centre  (JRC).  By  providing  appropriate  metrics  to  measure  and  monitor 
critical aspects of the digital transformation and of emerging digital technologies within their economic and 
technological  ecosystems,  and  by  investigating  technology-driven  innovation  and  change  in  a  global 
perspective,  the  IDITES  project  contributes  to  analysing  the  impacts  of  the  digital  transformation  on 
economies and societies. More specifically, the work presented in this report builds on the experience of the 
’Prospective  Insights  on  ICT  R&D’  (PREDICT)  project,  which  aims  at  providing  new  methodologies  and 
indicators  to  measure  and  analyse  the  digital  transformation  and  the  evolution  of  emerging  technological 
domains that are likely to impact future economic trajectories.  

This  work  continues  the  thread  of  research  on  the  Techno-Economic  ecoSystems  (TES)  methodological 
approach conducted within the framework of the PREDICT project. TES is a science-based analytical approach 
originally  developed  by  the  Digital  Economy  Unit  of  the  JRC  to  target  technology-based  dynamic  segments 
that  play  an  important  role  in  the  digital  transformation  but  tend  to  elude  official  statistics  or  standard 
classifications.  The  lack  of  indicators  adequate  to  capture  the  pervasiveness  and  evolution  of  emerging 
technologies  raises  a  call  for  new  analytical  instruments  and  metrics  to  better  inform  policies  for  a  more 
sustainable,  digital,  resilient  and  globally  competitive  European  economy.  TES  is  thus  a  suitable  tool  to 
describe and analyse dynamic emerging technologies those pervasiveness and evolution are better assessed 
using metrics and indicators capable to account for their holistic nature and complex structure.  

The  authors  would  like  to  acknowledge  the  contributions  from  several  colleagues.  First  of  all,  Josep  Soler-
Garrido,  who  contributed  to  the  conceptualisation  of  the  digital  areas  and  the  procedures  to  define  the 
perimeter of the digital ecosystem. The authors are also deeply grateful to Carlos Torrecilla Salinas, Michael 
Lutz,  Paul  Desruelle,  Emilia  Gómez,  Eva  Martinez  Rodriguez,  and  Juan  Torrecillas  Jodar  for  their  comments 
and their support through the realisation of this work. 

Authors 

Elisa Calza, European Commission, Joint Research Centre 

Alessandro Dalla Benetta, Piksel 

Uroš Kostić, FIncos 

Irena Mitton, MITGIS Croatia 

Miguel Vázquez-Prada Baillet, European Commission, Joint Research Centre 

Michele Carenini, Dedalus Spa 

Pierpaolo Cira, European Commission, Joint Research Centre  

Giuditta De Prato, European Commission, Joint Research Centre  

Riccardo Righi, European Commission, Joint Research Centre  

Michalis Papazoglou, European Commission, Joint Research Centre  

Montserrat López Cobo, European Commission, Joint Research Centre  

Melisande Cardona, European Commission, Joint Research Centre  

3 

 
 
 
 
 
 
 
4 

 
 
 
 
 
 
 
 
 
 
 
 
 
Executive summary  

Policy context 

A  period  of  uncertainty  and  fast-changing  global  scenarios  is  revealing  the  existence  of  disruptions  and 
bottlenecks in supply chains, markets, and technology transfer, exposing the  vulnerabilities of the  European 
digital  space  as  well  as  its  dependencies  on  non-European  technologies  and  players.  This  is  posing  new 
challenges that call for urgent strategic policy initiatives to ensure Europe continue pursuing the 
twin  transition.  In  this  regard,  the  European  Union  (EU)  is  realigning  its  economic,  industrial,  and 
technological ambitions. This requires the EU to assess and address any strategic dependencies, in order to 
reduce  reliance  on  third  countries  in  strategic  segments  and  critical  technologies,  create  a  technologically 
autonomous and sovereign Europe, and increase competitiveness in digital and green technologies, to better 
prepare for the challenges and opportunities of the twin transition.  

In  this  context,  technological  and  digital  leadership  remains  an  essential  driver  of  EU’s  competitiveness,  as 
digital technologies are reshaping the horizon of technology development and their mastering can grant the 
EU a leading role in the future global technological landscape. The successful implementation of the digital 
transformation  represents  the  condition  for  EU’s  present  and  future  digital  and  industrial 
leadership and sovereignty. 

Being the locus of  digital transformation, the digital ecosystem is at the centre  of the EU’s goals of 
EU’s digital policies. Digital technologies and the digital ecosystem have indeed gained a special position on 
current  policy  stage,  lying  at  the  intersection  of  major  EU  policy  initiatives  and  programmes  -  such  as  the 
Digital Decade, the New Innovation Agenda, the New Industrial Strategy – that aim at positioning Europe as a 
global  industrial  and  technological  leader,  making  the  EU  more  competitive  globally,  and  enhancing  its 
strategic  autonomy.  Understanding  and  analysing  the  digital  ecosystem,  alone  as  well  as  in  its 
overlapping  with  other  industrial  ecosystems,  has  become  a  necessary,  urgent  and  policy-relevant 
exercise.  Monitoring  its  evolution  is  also  key  to  leverage  a  successful  twin  transition  in  all  industrial 
ecosystems, and also allows insights on the other ecosystems’ evolution paths.  

About the report: objectives and audience 

This  report  proposes  a  way  to  explore  and  investigate  the  digital  ecosystem  from  a  policy-
relevant perspective. More specifically, the report describes the Digital Techno-Economic ecoSystem 
(DGTES), which is the application of the Techno-Economic ecoSystem (TES) analytical approach to the study 
of the digital ecosystem. In particular, the report: 

—  explains  the  DGTES  analytical  approach  and  the  steps  leading  to  the  construction  of  the  DGTES  graph 

database;  

—  discusses  the  aims,  originality,  and  advantages  of  this  analytical  approach  for  the  study  of  emerging 

technologies in dynamic techno-economic ecosystems, such as the digital ecosystem;  

—  presents a set of metrics and indicators that can be generated from the DGTES digital ecosystem dataset 

in order to provide a global overview and an analysis of the digital ecosystem; 

—  reflects  on  how  the  results  of  DGTES  can  provide  insights  to  policy  initiatives  aiming  at  strengthening 
EU’s  digital  leadership  and  sovereignty,  and  at  fostering  the  development  of  the  technological  and 
industrial capabilities needed for a vibrant digital ecosystem in Europe.  

This report targets a broad audience, which includes that part of the scientific community engaging in policy-
relevant  research  as  well  as  decision-  and  policy-makers.  The  style  and  the  content  of  the  report  try  to  be 
consistent with the aim of favouring its reading and understanding also by a non-technical audience, without 
sacrificing the complex nature and the scientific rigour that characterize the DGTES analytical approach.     

About DGTES: justifications and steps 

Segments of rapidly evolving and emerging technologies, such as digital technologies, are hardly captured by 
traditional metrics or classifications, which have a limited ability in detecting the complex structure and the 
web of interconnections and relationships that characterizes these dynamic technology-based environments. 
The lack of indicators adequate to capture the pervasiveness and evolution of emerging digital technologies 
raises a call for new analytical instruments to better understand the digital ecosystem and to inform policies 

5 

for  a  more  sustainable,  digital,  resilient  and  globally  competitive  European  economy.  Grounded  on  the  TES 
methodology  developed  by  EC  JRC  to  map  and  explore  dynamic  and  complex  techno-economic  segments, 
DGTES represents an appropriate analytical approach to responds to this call for an adequate tool to 
study, explore and better understand the complex landscape of digital technologies, and to timely 
inform policy initiatives of EU and Member States (MSs) for a technologically dynamic ecosystem such as 
the digital one. 

DGTES is characterized by various steps (Figure Es.1). DGTES proposes a methodology to collect and converge 
multiple  sources  of  microdata  into  a  harmonized  graph  database,  where  the  information  contained  in 
individual  datasets  is  enriched  and  amplified  by  the  possible  connections  across  them.  Giving  access  to  a 
comprehensive  set  of  relevant  information  on  the  digital  ecosystem,  this  DGTES  database  enables  the 
generation of a series of indicators and metrics that allow mapping and analysing the digital ecosystem by 
providing a compelling characterization of its main elements (i.e. actors, activities, interlinkages) and a better 
understanding of its features, structure and complexity. DGTES is a policy-relevant analytical instrument that 
can  contribute  to  informing,  assessing  and  ultimately  improving  the  policies  aiming  at  fostering  digital 
transformation and leveraging a successful twin transition. 

Figure ES 1. DGTES: main steps 

Source: Authors’ elaboration, Digital Economy Unit, JRC. 

DGTES in a nutshell 

The  Digital  Techno-Economic  ecoSystem  (DGTES)  is  the  application  of  the  TES  analytical  approach  to  the 
study of the digital ecosystem. The main aim of DGTES is to map the digital ecosystem and to provide 
an analysis of its elements, structure and features from a holistic, multidimensional and policy-
relevant perspective. Considering the  volume and  variety of sources  considered, and the amount of work 
needed to match through the data sources, DGTES represents one of the most accurate and comprehensive 
way to describe, map and analyse a dynamic and complex environment of emerging technologies such as the 
digital ecosystem.  

Figure  ES.2  and  ES.3  offer  a  representation  of  the  workflow  and  of  the  potential  outcomes  of  the  DGTES 
analytical approach described in the report. DGTES can be exemplified as a process unfolding along six phases 
– four describing the  DGTES  methodology (Figure ES.2) and two  related to the analysis and  how its  results 
can serve to inform EU policies (Figure ES.3). 

1.  Setting  the  technological  perimeter  of  the  digital  ecosystem,  by  identifying  its  main 

technological subdomains with the notion of digital areas 

Digital  areas  correspond  to  informal,  internally-coherent,  techno-economic  categories  used  to  cluster  digital 
technologies  that  relate  to  the  same  technological  domain  (e.g.  “Artificial  Intelligence”)  and/or  to  the  same 
type  of  application  or  final  use  (e.g.  eHealth  solutions).  The  15  digital  areas  considered  by  DGTES  are 
complaint  with  the  two  criteria  of  being  forward  looking  and  policy  relevant.  Each  digital  area  is  also 
associated  to  a  unique  list  of  keywords,  which  are  used  to  search  into  various  repositories  of  textual 
documents  (i.e.  patents,  journal  articles,  EU-funded  projects),  in  order  to  identify  those  documents  that  are 
relevant for the definition of the digital ecosystem (see following points).  

2.  Detecting the activities that define the digital ecosystem, by collecting and pre-processing the 

textual information contained in relevant documents 

DGTES  considers  as  relevant  those  activities  that  contribute  to  the  production  and/or  evolution  of  digital 
technologies – that is, that leave a ‘digital footprint’. In practice, an activity is associated to a document that 

6 

 
carries  textual  information  about  the  activity  itself  and  its  attributes.  The  documents  corresponding  to 
relevant  activities  are  detected  by  searching  for  given  lists  of  keywords  within  specific  repositories  (see 
previous point). In DGTES, relevant activities can be of three types: 

(a)  business  activities,  derived  from  information  on  companies’  core  business  and  on  the  production, 
supply  and/or  exchange  of  goods  and/or  services,  and/or  on  investments  and  funds  financing 
industrial and business initiatives (i.e. venture capital deals);  

(b)  innovation  activities,  corresponding  to  outputs  of  Research  &  Development  (R&D)  activities  in  the 
form of patenting initiatives (i.e. filing of priority patents) and/or participation in innovative research 
projects (i.e. EU-funded projects H2020 and FP7);  

(c)  research  activities,  reflecting  academic  contributions  of  frontier  research,  such  as  publications 

and/or participation in high level international conferences.  

3. 

Identifying  players  engaging  in  relevant  activities,  by  cleansing  and  processing  the  textual 
information contained in relevant documents 

In DGTES, the term player defines any economic (market and non-market) actor involved in digitally-relevant 
activities.  More  specifically,  players  engaging  in  the  digital  ecosystem  are  identified  from  the  documents 
corresponding  to  the  relevant  activities  (see  previous  point)  after  a  multi-step  process  of  data  cleansing, 
which includes (among others) disambiguation, classification, and validation. Relevant players can be of three 
types:  

          (a)  companies and firm;  

          (b)  academic institutions and research centres;  

          (c)  governmental authorities and bodies.  

4.  Generating the graph database of the digital ecosystem, built on the interlinkages connecting 

activities and players 

The  analysis  of  the  textual  information  in  the  relevant  documents  allows  connecting  activities  and  players, 
thus identifying how players relate to each other and are interlinked through their shared activities or other 
attributes  (i.e.  colocation).  The  DGTES  database  also  contains  information  about  the  players  participating  in 
the detected activities and about their features and attributes (i.e. their organizational type and geographical 
location).  The  web  of  connections  built  on  all  these  elements  and  information  defines  the  structure  of  the 
DGTES graph database, which ultimately allows mapping the digital ecosystem.   

Figure ES.2. DGTES workflow and methodology – Phases 1 to 4 

Source: Authors’ elaboration, Digital Economy Unit, JRC. 

7 

 
5.  Calculating indicators to map and analyse the digital ecosystem 

DGTES allows generating a series of indicators that can help disclose, investigate and better understand the 
digital  ecosystem.  These  can  be  grouped  in  different  sets  according  to  their  goals  and  to  dimensions  of 
analysis:  

(a)  indicators  that  provide  an  overview  of  the  digital  ecosystem  global  landscape,  with  its  main 

elements and dimensions;  

(b)  indicators that describe and analyse the network nature of the digital ecosystem (i.e. collaboration 

metrics, network statistics, graph properties). 

The high level of granularity is a crucial feature of the DGTES database, which allows grasping a finer picture 
of the elements of the ecosystem and of their attributes (i.e. focus on a specific organizational type, such as 
firms and companies). Moreover, most of the indicators can be calculated for each year in the covered period, 
thus allowing to add a longitudinal and time-varying perspective to the analysis of the digital ecosystem. 

6. 

Informing policies for digital transformation and twin transition 

Policies  aiming  at  fostering  the  digital  transition  and  reducing  Europe’s  digital  dependency  need  to  be 
grounded on a better and more comprehensive understanding of the digital ecosystem. In this regard, DGTES 
can  offer  novel  evidence  on  the  positioning  of  the  EU  in  terms  of  leadership  and  competitiveness  in  the 
domain  of  emerging  and  critical  digital  technologies,  informing,  assessing  and  informing  policy  initiatives 
fostering digital transformation and helping Europe successfully navigate towards a more sustainable, digital, 
resilient and globally  competitive economy. The results of DGTES can even contribute  to the formulation of 
concrete policy instruments, such as a toolbox of interventions to mitigate EU strategic dependencies in key 
industries and critical digital subdomains. 

Figure ES 3. DGTES for EU policy priorities – Phases 5 to 6 

Next steps and future work  

Source: Authors’ elaboration, Digital Economy Unit, JRC. 

Future works using DGTES will enlarge the scope of the analysis to embrace more dimensions and aspects of 
the digital ecosystem, and beyond it. Just to mention few possible avenues of future research, these  works 
could  look  at  the  technological  features  of  the  digital  ecosystem  with  the  application  of  Natural  Language 
Processing (NLP) methods like topic modelling; explore the drivers of industrial competitiveness and see how 
certain features of the digital ecosystem are related to the industrial performance; analyse the intersections 
between the digital ecosystem and the other 13 industrial ecosystems identified by the Industrial Strategy.  

8 

 
1 

Introduction  

A  new  global  economic  and  industrial  landscape  is  emerging  from  the  series  of  shocks  that  have  shaken 
societies and economies during the past few years.  

Openness to trade and investments has been a major source of growth for the EU (European Union), which 
still  remains  a  major  importer  and  exporter.  However,  the  COVID-19  pandemic  crisis  has  revealed  the 
existence of economic and industrial strengths as well as of bottlenecks, as Europe has been severely tested 
by  supply  restrictions,  border  closures  and  fragmentations  following  the  COVID-19  outbreak.  The  pandemic 
has indeed exposed the vulnerabilities of the European digital space and its dependencies on non-European 
technologies.  The  vulnerability  of  interdependent  entities  to  disruptions  in  value  chains,  trade  flows  and 
technology  transfer  has  become  even  more  evident  since  the  escalation  of  the  armed  conflict  in  Ukraine, 
whose severe consequences are reinforcing the urgency of prompt strategic policy changes to ensure the EU's 
peace, prosperity and security, today as well as in the future. 

Europe  is  realigning  its  economic  and  industrial  ambitions  to  take  into  account  these  new  global 
circumstances. With the awareness that this calls for a  better grip and  understanding of where  current and 
future strategic dependencies lie, Europe is redefining its competitive edge and its position as one main player 
on  the  global  landscape.  Going  hand-in-hand  with  the  need  to  steer  recovery  towards  a  more  sustainable, 
digital, resilient and globally competitive industry and economy, current global contingencies require Europe to 
increase its competitiveness in digital and green technologies in order to better prepare for the challenges and 
opportunities of the twin transition.  

In this  context, technological leadership  remains an essential driver of EU’s competitiveness and innovation. 
Now  more  than  ever  the  EU  needs  to  leverage  its  position  as  global  industrial  and  technological  leader  to 
reduce  reliance  on  third  countries  in  strategic  segments  and  create  a  technologically  autonomous  and 
sovereign Europe. In particular, EU’s ability to keep the pace with the emergence of dynamic technology-based 
segments plays a fundamental role in defining Europe's role in the technological and industrial scene and in 
shaping its current and future social and economic trajectories.  

As  a  dynamic  technological  domain  horizontally  affecting  the  whole  economy,  digital  technologies  are 
attracting increasing attention as key drivers of economic growth, competitiveness, and economic resilience. 
The  rapid  evolution  of  digital  technologies  is  catalysing  the  digital  transformation,  intended  as  the 
fundamental and deep transformation of market and non-market activities and processes by means of digital 
technologies,  leading  to  fundamental  changes  in  the  way  to  operate,  produce  and  deliver  value.  The  digital 
transformation had already been leading to deep changes in the economy and the society. Still, the COVID19 
pandemic crisis increased even more this interest for the impact of digital technologies on economic resilience 
and industrial recovery, and reinforced the perception of their crucial role, as these technologies have become 
an  imperative  for  working,  learning,  entertaining,  socialising,  shopping,  delivering  services  and  accessing 
everything  from  health  services  to  culture.  This  accelerated  pace  of  digital  transformation,  triggered  by  an 
unprecedented  demand  for  digital  technologies,  calls  for  a  better  understanding  of  its  pervasiveness  and 
benefits.  

The  successful  implementation  of  the  digital  transformation  is  the  condition  for  EU’s  present  and  future 
digital and industrial leadership. It is thus not surprising that the digital transformation has come to occupy a 
central place in EU’s policy space. ‘A Europe fit for the digital age’ 2 is one of the six European Commission’s 
priorities for the period 2019-2024. It represents the EU’s Digital Strategy and envisions Europe as a strong 
and sovereign digital player, placing the digital transformation at the centre of EU’s future to make it work for 
people and businesses while helping to achieve the European Green Deal target of a climate-neutral Europe 
by 2050 3. Following the footsteps of the Digital Strategy, in 2021 the Commission presented Europe’s Digital 
Compass and the Policy Programme ‘Path to the Digital Decade’4 to translate EU digital vision into concrete 
targets  and  to  ensure  that  Europe  achieves  a  successful  twin  transition  by  2030.  This  Policy  Programme 
remarks  Europe’s  ambition  to  make  the  following  years  ‘the  digital  decade’,  by  reinforcing  its  digital 
leadership  in  an  open  and  interconnected  world  and  by  pursuing  digital  policies  that  foster  EU  digital 

(2)   See  the  webpage  of  ‘A  Europe  fit  for  the  digital  age’  (https://ec.europa.eu/info/strategy/priorities-2019-2024/europe-fit-digital-

age_en) and the Commission’s communication ‘Shaping Europe’s digital future’ (COM (2020)67 final). 

(3)   See the Commission’s communication ‘The European Green Deal’ (COM(2019) 640 final). 
(4)   See the Commission’s communication ‘2030 Digital Compass: the European way for the Digital Decade’ (COM (2021) 118 final) and 
the Decision of the European Parliament and of the Council to establish the 2030 Policy Program ‘Path to the Digital Decade’ (COM 
2021) 574 final). 

9 

                                                        
 
sovereignty  while  empowering  people  and  businesses  to  seize  a  human-centred,  sustainable,  and  more 
prosperous  digital  future.  Consistently  with  this  vision,  the  Recovery  and  Resilience  Facility  (RRF)5 
acknowledges  that  digital  technologies  are  key  factors  for  economic  recovery  and  resilience.  Aware  that 
bouncing  back  from  the  COVID19  crisis  represents  an  opportunity  to  accelerate  digital  transformation,  this 
policy  instrument  promotes  reforms  and  investments  in  digital  technologies,  digital  skills,  services  and 
infrastructures to improve EU competitiveness.  

Also the EU 2020 New Industrial Strategy and its updated version 6, launched in 2021 to take into account the 
implications of the COVID-19 crisis, are fully in line with the mentioned policy initiatives’ visions and goals of 
EU  digital  sovereignty  and  global  leadership.  Pursuing  an  ambitious  European  industrial  policy,  the  New 
Industrial Strategy lays out a plan to leverage Europe's position as a global industrial leader by supporting the 
twin transitions, making EU industry more competitive globally, and enhancing its strategic autonomy. In this 
regard,  the  Industrial  Strategy  makes  clear  that  attaining  EU’s  digital  ambitions  requires  to  assess  and 
address  any  strategic  weaknesses,  vulnerabilities  and  strategic  dependencies  in  key  areas  where  Europe 
depends on its global competitors, such as in the domain of digital technologies related to online platforms, 
semiconductors,  cloud  computing,  and  in  critical  inputs  necessary  for  the  green  and  digital  transitions.  EU 
digital  sovereignty  and  leadership  need  to  be  grounded  on  the  achievement  of  strategic  autonomy,  both 
industrial and technological.  

As well as focusing on technologies and strategic dependencies, the Industrial Strategy also promotes a new 
approach  to  think  of  economic  activities  in  terms  of  industrial  ecosystems.  Industrial  ecosystems  can  be 
defined as interdependent techno-economic spaces where activities take place in an interconnected way and 
with  the  participation  of  heterogeneous  actors  pursing  different  socioeconomic  goals.  14  industrial 
ecosystems7, covering a whole range of economic activities and purposes, have been identified based on their 
economic and technological relevance, and on their expected contribution to the decarbonisation, digitalisation 
and resilience of the EU economy.  

Among  these  14  ecosystems,  the  digital  ecosystem  presents  its  own  peculiarities.  It  is  where  digital 
transformation takes place and shape: here the processes of creation, development and production of digital 
technologies  are  triggered  and  moulded  by  interactive  and  complex  dynamics,  but  at  the  same  time  the 
structure of this ecosystem and the composition of its stakeholders are influenced by the evolution of digital 
technologies, in a feedback-loop mechanism. The digital ecosystem is, therefore, characterized by a dynamic 
technological  change  and  particularly  complex  interlinkages.  The  digital  ecosystem  is  also  transversal  and 
horizontal by nature. Digital technologies currently represent the substrate of any technical solution and have 
transversal applications in any activity, thus making the digital ecosystem horizontal to all other technological 
and industrial ecosystem. Moreover, the extreme permeability of all activities to digital technologies implies 
that  the  digital  ecosystem  is  not  simply  intersecting  but  that  it  is  also  transforming  the  other  industrial 
ecosystems from inside, by defining the current and future technological scope in which they operate. In this 
respect,  the  digital  ecosystem  is  forward-looking,  as  it  already  incubates  the  technological  trajectories  that 
will shape the economy in the future. Analysing the digital ecosystem and monitoring its evolution is therefore 
key to leverage a successful twin transition in all industrial ecosystems, and also allows insights on the other 
ecosystems’ evolution paths.  

Being the locus of digital transformation, the digital ecosystem lies at the centre of the current policy stage, 
at  the  intersection  of  major  EU  policy  initiatives  and  programmes,  such  as  the  Industrial  Strategy  and  the 
Digital  Decade.  This  report  focuses  on  the  digital  ecosystem  and  on  how  this  can  be  investigated  from  a 
policy-relevant perspective – that is, in a way that contributes to informing and assessing  policies aiming at 
transforming Europe into a more sustainable, digital, resilient and globally competitive economy.  

Understanding the elements and the features of the digital ecosystem, alone as well as in its overlapping with 
other  industrial  ecosystems,  and  analysing  and  monitoring  its  evolution,  is  indeed  a  necessary,  urgent  and 

(5)   The Recovery and Resilience Facility is the key instrument at the heart of NextGenerationEU to mitigate the impact of the COVID-19 
pandemic  crisis  and  help  the  EU  emerge  stronger  and  more  resilient.  For  more  information,  see  Commission’s  communication 
‘Europe's moment: Repair and Prepare for the Next Generation’ (COM (2020) 456 final). 

(6)   See the Commission’s communications ‘A new Industrial Strategy for Europe’ (COM(2020) 102 final) and ‘Updating the 2020 New 

Industrial Strategy: Building a stronger Single Market for Europe’s recovery’ (COM(2021) 350 final).  

(7)   The 14 industrial ecosystems are: Aerospace & Defence, Agri-food, Construction, Cultural and Creative Industries, Digital, Electronics, 
Energy Intensive Industries, Energy-Renewables, Health, Mobility-Transport-Automotive, Proximity, Social Economy and Civil Security, 
Retail, Textiles, and Tourism. They represent approximately 70% of the EU economy and 80% of the business economy (as a share 
of value added) (COM(2020) 102 final). 

10 

                                                        
 
policy-relevant  exercise.  Yet,  the  absence  of  standard  classifications  and  of  indicators  that  can  adequately 
capture the features of such a dynamic and complex ecosystem prevents from measuring the pervasiveness 
and evolution of digital technologies. This lack raises a call for new indicators and analytical instruments to 
better  understand  the  digital  ecosystem  and  to  properly  support  the  implementation  of  policy  initiatives 
accounting for its dynamics and complex interlinkages 

The Techno-Economic ecoSystem (TES) approach represents an appropriate methodological choice to analyse 
the complex landscape of digital technologies. TES is a science-based analytical approach developed by the 
Digital Economy Unit of the European Commission’s (EC) Joint Research Centre (JRC) to investigate integrated 
and  technology-based  dynamic  segments  that  play  an  important  role  in  the  evolution  of  economies  and 
societies but tend to elude available official statistics or classifications. The TES methodology can provide a 
comprehensive  representation  of  the  industrial  ecosystems,  with  indicators  that  reflect  their  heterogeneous 
elements  and  interlinkages  as  well  as  their  nature  of  holistic,  complex  systems.  It  also  allows  detecting 
emerging technological domains as well as knowledge flows and bottlenecks, offering timely policy-relevant 
insights. The TES approach is an ecosystem by design; this methodology can thus be applied to the analysis of 
any dynamic and rapidly evolving technological, techno-economic, and industrial ecosystem. 

The  Digital  Techno-Economic  ecoSystem  (DGTES)  is  the  application  of  the  TES  analytical  approach  to  the 
study  of  the  digital  ecosystem.  Providing  a  compelling  characterisation  of  its  key  elements  (i.e.  actors, 
activities,  interlinkages),  features  and  structure,  DGTES  maps  the  digital  ecosystem  and  contributes  to  its 
better description and comprehension, in Europe as well as worldwide, at regional as well as at country and 
local  level.  This  analytical  approach  also  allows  looking  at  the  characteristic  of  the  networks  resulting  from 
shared  activities,  collaborations  and  co-location,  and  highlighting  knowledge  flows  and  gatekeepers,  latent 
communities, hubs, and emerging clusters of excellence. 

In  this  regard,  DGTES  represents  a  forward-looking  approach:  by  exploring  the  network  of  knowledge  and 
diffusion  of  digital  technologies  at  global  level  as  well  as  within  the  EU,  it  can  reveal  weak  signals  of 
emerging digital subdomains and monitor the evolution of the international industrial and research landscape, 
at the same time identifying opportunities and sources of competitiveness, and detecting vulnerabilities and 
obstacles that could compromise EU’s leadership in the current and future global scenarios. Shedding light on 
the worldwide networks of stakeholders and technological subdomains, DGTES can offer novel evidence about 
the  strategic  positioning  of  EU  and  of  individual  Member  States  (MSs)  in  the  realm  of  emerging  digital 
technologies,  and  about  how  to  foster  the  development  of  a  vibrant  digital  ecosystem  in  Europe.  Hence, 
DGTES responds to the call for an adequate tool to inform EU policy initiatives on the evolution and technical 
uptake in the very  dynamic technological domain of  digital technologies, and can  significantly contribute to 
the debate on competitiveness and technological and industrial leadership of the EU in the perspective of the 
EU polices such as the Industrial Strategy and Digital Decade.  

The  goal  of  this  report  is  to  describe  the  application  of  JRC’s  TES  methodology  to  the  study  of  the  digital 
ecosystem  (DGTES)  and  to  explain  the  steps  leading  to  the  construction  of  the  DGTES  digital  ecosystem 
database  while  highlighting  the  originality  and  the  advantages  of  this  approach.  The  report  also  introduces 
some of the metrics and indicators that can be generated based on the DGTES digital ecosystem database in 
order  to  map  the  digital  ecosystem  and  to  analyse  it  from  a  policy-relevant  perspective.  In  this  regard,  the 
report highlights how this methodology can help reveal emerging technological trajectories, latent knowledge 
flows  and  communities,  gatekeepers  and  hubs  within  the  ecosystem,  by  looking  at  the  hierarchy  of  the 
network  connections  and  interlinkages  resulting  from  shared  activities,  locations  and  technological  fields. 
Finally, the report reflects on how DGTES can provide policy-relevant insights. 

The report is structured as follows. Section 2 presents the general aims of TES and discusses the motivations 
for choosing this analytical approach, highlighting the gap in the conventional methodologies and metrics in 
capturing  and  measuring  the  complexity,  pervasiveness  and  evolution  of  dynamic  techno-economic 
ecosystems. Section 3 describes the DGTES, which is the application of the TES methodological approach to 
the digital ecosystem. This section explains the processes of data collection and cleansing, and discusses the 
procedures  used  to  identify  the  technological  subdomains  (digital  areas)  that  determine  the  technological 
perimeter of the digital ecosystem, and to generate the final DGTES database. Section 4 introduces some of 
the  indicators  and  measures  that  can  be  generated  from  the  DGTES  database,  in  order  to  provide  a 
characterisation of the key elements and features as well as a worldwide overview of the digital ecosystem. 
This  section  also  points  out  the  future  directions  of  analysis,  stressing  the  potential  of  DGTES  in  exploring 
more dimensions and aspects of the digital ecosystem as well as beyond it. Section 5 concludes by discussing 
the policy-relevance of the DGTES approach and how it can be extended to the analysis of other ecosystems.  

11 

2  TES: grasping the complexity of dynamic Techno-Economic ecoSystems  

This  section  introduces  and  discusses  the  Techno-Economic  ecoSystems  (TES)  analytical  approach  designed 
by  the  Digital  Economy  Unit  of  the  European  Commission’s  (EC)  Joint  Research  Centre  (JRC)  to  map  and 
explore  complex  techno-economic  ecosystems  of  dynamic  and  emerging  technologies.  The  first  subsection 
presents the aims, features and scope of the TES methodology. It also justifies the choice of this approach for 
the analysis of segments of emerging technologies from a policy-relevant perspective. The second subsection 
discusses  the  limitations  of  the  TES  as  analytical  tool,  highlighting  what  falls  beyond  its  original  aim  and 
scope.  

2.1  A new instrument to map Techno-Economic ecoSystems 

The  TES  analytical  approach  was  developed  by  EC  JRC  to  map  and  explore  dynamic  and  complex  techno-
economic segments and ecosystems of emerging technologies that are rapidly evolving and are expected to 
influence  competitiveness  and  economic  growth,  but  their  evolution  and  pervasiveness  are  hardly  analysed 
and assessed using available official statistics or standard classifications.  

These  techno-economic  segments  are  dynamic  environments  characterized  by  being  based  on  emerging 
technologies, accelerated technological change, and complex interlinkages. These technologies are reshaping 
the horizon of technology development and their mastering can grant the European Union (EU) a leading role 
in  the  future  global  technological  landscape.  Still,  their  complex  structure  is  hardly  captured  by  traditional 
metrics  or  classifications,  which  have  a  limited  ability  in  detecting  the  relational  dimensions  of  these 
technology-based environments, where one  part can influence the  behaviour of other  actors and shape the 
whole  structure.  Conventional  indicators  not  only  fail  to  adequately  detect  the  web  of  interconnections  and 
relationships  within  these  techno-economic  segments,  but  also  to  track  the  dynamics  of  knowledge  flows 
within and across geographical boundaries as well as within and across technological domains.  

The  shortcomings  of  traditional  indicators  are  more  evident  in  the  case  of  segments  of  rapidly  emerging 
technologies. The fast evolution of these technologies generates new sectors and revolutionize existing ones 
through the creation of new processes, new products, and new linkages within and across industrial activities. 
In  such  a  dynamic  techno-economic  context,  traditional  industrial  classifications  based  on  sets  of 
homogeneous activities (i.e. NACE) are exhausting their explanatory power: they are less suitable to describe, 
investigate  and  explore  processes  and  products  that  are  increasingly  shaped  by  interconnections  across 
sectors and by more technological content. This transformation of industrial production is due to the rise of 
transversal  multi-purpose  technologies  like  digital  technologies,  which  are  permeating  all  processes  and 
operations,  and  whose  industrial  application  is  enforcing  more  and  new  connections  across  different 
production activities and actors, even across industrial sectors. This is leading to increasingly interconnected 
and interdependent chains of value creation in what have become multisector production processes.  

In addition, the surge of deep tech innovations, resulting from the convergence between digital technologies 
and  scientific  fields,  is  strengthening  the  relationship  between  science,  technological  innovation  and 
production,  making  research  and  innovation  (R&I)  more  and  more  integrated  into  industrial  activities.  The 
increasing importance of deep tech as a new wave of innovation for Europe has been clearly remarked by the 
Commission’s  communication  ‘A  New  European  Innovation  Agenda’  (COM  (2022))332  final),  considering  its 
potential to thrive and deliver breakthrough solutions to address key societal challenges and to accelerate the 
transformation into a greener, knowledge-based, and resilient economy. This implies that factors that are now 
determining the future evolution of industrial activities - as it is the case of R&I-related factors - fall outside 
the  traditional  industrial  and  product  classification,  which  was  not  designed  to  take  into  account  these 
research-production linkages. This is further diminishing the suitability of traditional industrial classifications 
for forward-looking analytical purposes.  

The  2020  New  Industrial  Strategy  gave  an  important  contribution  to  ‘breaking  the  silos’  of  traditional 
industrial sectors, moving away from a narrow focus on industry defined as set of producers of homogeneous 
products towards a broader  approach  based on industrial ecosystems. These ecosystems can  be defined as 
interdependent  techno-economic  spaces  where  all  players  are  involved  in  the  achievement  of  a  certain 
socioeconomic  goal  and  where  activities  take  place  in  an  interconnected  way  -  from  the  smallest  start-ups 
and  the  largest  companies  cooperating  to  satisfy  a  new  market  need,  the  research  activities  supporting 
industrial  innovation  in  firms,  the  regulators  steering  economic  activity  through  conducive  policies,  to  the 
services providers and suppliers (COM(2020)102 final). The novelty of this approach is that it incorporates the 
systemic  importance  of  all  the  horizontal  and  vertical  links  among  economic  actors,  recognising  the  role  of 

12 

those  activities  often  considered  as  ancillary  to  industry,  such  as  R&I.  The  New  Industrial  Strategy  calls  for 
new  indicators  to  monitor  the  competitiveness  of  the  EU  economy  as  a  whole  and  as  it  emerges  from  the 
connections across industrial ecosystems (COM(2021) 350 final). The strategy also emphasizes the need of 
appropriate analytical tools to identify areas of strategic dependencies, in particular in sensitive ecosystems, 
which could threaten EU industrial and digital sovereignty.  

It  has  thus  become  urgent  to  rethink  how  to  analyse  the  industrial  ecosystems  in  order  to  improve  our 
understanding  of  industrial  dynamics  in  a  context  characterized  by  increasing  scientific,  technological  and 
productive interconnections and by the transversal application of and multi-purpose digital technologies. This 
need for indicators and analytical instruments to account for interlinkages and evolutions within and across 
industrial  ecosystems  is  policy  relevant:  it  responds  to  a  call  for  more  evidence-based  insights  to  support 
policies shaping the industrial ecosystems, which can be done only through indicators that can properly grasp 
their complexity, dynamic and integration. 

The TES analytical approach  responds to this call for new instruments to map, analyse and  understand the 
techno-economic  ecosystems  not  otherwise  systematically  covered  by  available  traditional  statistics.  TES 
does so by embracing a systemic perspective that can reveal the interlinkages and interactions across players 
as well as measure the digital transformation of the economy (Figure 1). 

TES is ‘ecosystem by design’. TES is designed to reveal the complex structure of the interlinkages connecting 
its elements (i.e. actors and activities)8, allowing to draw the ecosystems as they emerge from the collected 
information  about:  who  the  key  players  are,  which  different  roles  they  play  in  the  ecosystem,  in  which 
technologies they specialise, how they are geographically distributed, how they are connected to each other, 
how  innovation  spreads  through  the  networks.  TES  is  a  methodology  appropriate  to  describe  and  analyse 
techno-economic segments that function as a complex system. In this respect, it also allows looking at how 
players  participate  in  implicit  relations  and  tacit  processes  of  learning  and  knowledge  exchange.  This 
methodological approach is thus particularly suitable for ecosystems where knowledge creation and diffusion 
take a network structure, such as in the case of ecosystems of multi-purpose and horizontal technologies like 
digital ones.  

TES  is  a  science-based  analytical  approach  grounded  on  a  micro-based  perspective.  It  is  conceived  as  a 
replicable  methodology  to  map  and  analyse  technology-driven  ecosystems  by  exploiting  a  wide  range  of 
factual  data  from  non-official  heterogeneous  sources,  bridging  the  needs  of  describing,  monitoring  and 
benchmarking these ecosystems with quantitative analysis and scientific evidence. The TES methodology also 
allows for a certain flexibility in the scope of analysis: the fact that it relays on data collected at a micro-level 
makes it possible to exploit the high granularity of the data and to add more and finer levels of analysis - for 
instance, to explore  different  geographical levels (e.g.,  cities,  countries, regions) or zoom on  specific  players 
(e.g. firms or research institutions)..  

TES is a forward-looking approach. It allows detecting weak signals of emerging technological domains that 
are  likely  to  shape  the  future  trajectory  of  knowledge  flows  and  production  but  whose  technological  and 
industrial application is still evolving. This characteristic of TES is particularly useful when mapping knowledge 
flows in order to detect the emerging thematic layers of technologies and to identify which actors occupy a 
strategic position and control the networks of knowledge flows and production in these technological domains. 
TES can be relevant from the perspective of European strategic autonomy, as it can contribute to the analysis 
and assessment of the risk of vulnerabilities arising from European economies’ strategic dependencies.  

Finally,  TES  is  policy  relevant.  The  TES  analytical  approach  responds  to  the  call  for  a  policy-relevant 
methodology that can provide quantitative indicators and metrics adequate to map the geographic presence, 
technological  development,  impact,  and  evolution  of  complex  and  dynamic  techno-economic  ecosystems. 
Based  on  a  rigorous  scientific  methodology,  the  results  of  the  application  of  TES  to  an  ecosystem  can 
contribute to informing and shaping policy-relevant recommendations aiming at fostering EU’s technological 
and industrial leadership. 

(8)  Section 3 “DGTES: applying the TES methodological approach to the digital ecosystem” provides a more precise definition of which 

are the actors and the activities in an ecosystem analysed with the TES methodology.  

13 

                                                        
 
Figure 1. The TES analytical approach: specific features    

Source: Authors’ elaboration, Digital Economy Unit, JRC. 

In  sum,  the  TES  analytical  approach  can  provide  a  timely  representation  of  integrated  ecosystems  of 
emerging  technologies  that  play  an  important  role  in  the  evolution  of  economies  and  societies,  offering  a 
synthetic  but  analytical  and  policy-relevant  overview  of  their  elements,  structure,  and  dynamics.  TES  is  an 
appropriate instrument for the analysis of technologically dynamic environments that can be better described 
and assessed using metrics and indicators that account for their holistic nature and complex structure. Box 1 
summarizes  the  main  aspects  and  dimensions  of  techno-economic  ecosystems  that  can  be  mapped  and 
analysed using the TES approach. 

Box 1. TES: mapping the landscape of dynamic techno-economic ecosystems from a policy-relevant perspective 

The Techno-Economic ecoSystems (TES) analytical approach allows  mapping the landscape  characterizing a 
dynamic techno-economic ecosystem. More specifically, the application of TES can: 

- Identify main stakeholders 

Using several sources of microdata, both horizontal covering the whole economy and vertical to the analysed 
technological domain, TES generates a comprehensive database containing detailed information on activities 
and players, answering the questions: Who are the key players? Which activities do they engage in? 

- Draw the competitive international scenario  

Relying on information on individual  players’ location, TES allows looking at the geographical  distribution of 
activities  and  players  at  different  levels  (worldwide,  regional,  local),  addressing  the  following  issues:  Where 
are players and activities located and geographically distributed? How different are the geographical regions 
in terms of the ‘critical mass’ of players and activities they display? Which geographical regions are leading in 
terms of activities performed/players hosted? Which geographical regions are more active in sharing research 
and  innovation  activities?  Which  role  does  a  geographical  region  play  in  the  worldwide  network,  also  with 
respect to brokering knowledge specific to sub-technologies? 

- Detect key thematic areas and technological domains or subdomains 

TES allows detecting the main thematic areas and technological domains and subdomains that characterize 
an  ecosystem.  By  relating  these  with  geographical  distribution  of  players,  TES  allows  defining  the 
technological profile of the ecosystem at different levels (worldwide, regional, local hotspots), addressing the 
following issues: Which thematic areas dominate the ecosystem? In which areas, domains or subdomains do 
activities  concentrate?  In  which  areas,  domains  or  subdomains  is  a  certain  geographical  area  more 
competitive  and/or  specialized?  Which  areas,  domains  or  subdomains  do  emerging  technologies  cluster 
around?   

- Describe industrial composition and capacity  

TES  can  provide  an  overview  on  industrial  capacity  and  composition  in  the  ecosystem,  by  relating  the 
information  on  firms’  features  (i.e.  size,  organizational  type,  main  sector  of  activity,  location,  performance, 
technological domain of main activities) to answer the questions: How active are firms in the ecosystem? How 
is the profile of the firms engaging in the ecosystem?   

14 

 
- Reveal patterns of collaboration 

TES reveals the networks of collaborations and relations across players in the ecosystem. By considering their 
geographical and technological dimensions, TES can describe this collaborative network and its characteristics, 
addressing the following issues: How are players  connected to each other? Across which  regions?  How does 
innovation  spread  through  the  network?  Which  players  are  leading  in  terms  of  collaborative  activities  and 
where  are  they  located?  Who  controls  knowledge  flows  and  production  (gatekeepers)  and  where  are  they 
located? How were such knowledge flow evolving over time, what can be expected future trends? 

The TES  methodology  has so far been applied to the study of segments of emerging technologies that are 
rapidly  evolving  and  are  expected  to  have  a  high  impact  on  economies  and  societies,  such  as  photonics 
(Samoili et al. 2019), earth observations technologies (Pogorzelska et al. 2019), and artificial intelligence (AI) 
(De  Prato  et  al.  2019,  Righi  et  al.  2021)9.  In  these  studies,  the  TES  approach  proved  its  ability  to  map  and 
analyse  the  features  and  functions  of  ecosystem  of  these  dynamic  emerging  technologies,  respecting  their 
complex  nature  and  taking  into  account  their  heterogeneous  players  and  activities  as  well  as  their 
interlinkages and networks.  

Being  an  ecosystem  by  design,  the  TES  methodological  approach  can  be  applied  to  any  dynamic  techno-
economic  ecosystem.  This  is  the  case  of  the  digital  ecosystem,  which  is  the  techno-economic  space  where 
digital  technologies  take  shape  and  evolve.  Digital  technologies  are  extremely  policy  relevant,  since  their 
evolution is catalysing the digital transformation. However, as in the case of other emerging and transversal 
technologies,  the  absence  of  adequate  indicators  challenges  the  possibility  to  measure  and  analyse  the 
pervasiveness,  evolution  and  impact  of  digital  technologies.  Better  understanding  the  forces  driving  digital 
transformation has become an urgent imperative, as succeeding in Europe’s digital and green twin transitions 
is one the main policy priority of the European Commission.  

The  application  of  the  TES  analytical  approach  to  the  digital  ecosystem  (DGTES)  can  serve  the  purpose  of 
having  methodologies  and  instruments  suitable  to  describe  and  analyse  the  global  landscape  of  digital 
technologies  from  a  policy-relevant  perspective,  allowing  to  grasp  both  features  of  complexity  and  of 
technological dynamism of the digital ecosystem. In the perspective of the EU Industrial and Digital Strategies 
(discussed in the Introduction), DGTES can help deal with issues related to digital transformation and to the 
mitigation of strategic dependencies in critical industries, contributing to the formulation of policies fostering 
EU digital and industrial leadership in the current and emerging technological landscape.  

2.2  Beyond the scope of the TES analytical approach 

The  TES  analytical  approach  was  developed  to  map  and  analyse  complex  techno-dynamic  ecosystems  of 
emerging technologies. Despite encompassing several dimensions and layers of analysis and comprehensively 
exploring  various  aspects  of  these  ecosystems,  the  explanatory  power  of  TES  is  constrained  in  the  case  of 
environments whose structure, composition and thematic outreach the ones of a techno-dynamic ecosystems. 
It  is  therefore  useful  to  point  out  the  cases  falling  out  of  the  analytical  scope  of  the  TES  methodological 
approach.  

The TES approach is mostly based on codified knowledge (i.e. patents, publications, project funds, description 
of industrial activities) and relies on both proprietary and public information that is formalized and accessible. 
Although  it  allows  drawing  implicit  or  tacit  networks  of  potential  relations  based  on  information  on 
geographical  or  technological  proximity  (i.e.  latent  communities),  the  TES  methodology  has  a  limited 
explanatory power about the elements of an ecosystem that that cannot be explicitly observed, because they 
correspond,  for  example,  to:  knowledge  protected  by  industrial  secrets  (thus,  not  publicly  shared);  type  of 
transactions not reflected in  accessible data; activities performed by  very small enterprises, etc. Even if the 
list  of  sources  can  be  extended  further,  both  for  what  concerns  with  horizontal  sources  at  global  level  and 
vertical sources specific to selected technologies, several financial transactions as well as technology transfer 
activities may take place without releasing adequate data allowing to reflect and monitor them. These data 
are, therefore, invisible to the TES methodology. 

(9)   See the JRC Technical Reports ‘The AI Techno-Economic Segment Analysis’ (De Prato et al. 2019), ‘The techno-economic segment of 
Earth  observation’  (Pogorzelska  et  al.  2019),  and  ‘Unveiling  Latent  Relations  in  the  Photonics  Techno-Economic  Complex  System’ 
(Samoili et al. 2019). 

15 

                                                        
 
The  TES  methodology  defines  the  techno-economic  ecosystems  from  a  supply-side  perspective.  TES  is 
designed to identify players engaging in industrial, research or innovation activities related to the production 
of  knowledge,  goods  or  services  within  a  certain  technological  domain,  whose  ultimate  outcome  is  to  push 
further the technological frontier. Hence, by design the TES approach is not suitable to explore the demand-
side and the micro-level mechanisms underlying technology adoption in a certain technological domain. 

The TES methodology detects only a little part of the mechanism of financing and investments that take place 
within an ecosystem. For instance, in its current version, TES covers publicly funded research only at EU level, 
but it disregards public funding initiatives at national level. While TES captures as much as possible funding 
deals and investments (for example, it also relies on information on venture capital funds), this approach may 
miss other important sources of financial inputs and resources, which makes it unable to univocally associate 
the whole amounts invested in an ecosystem with its activities and outputs. TES is thus inadequate to be used 
as financial assessment tool for a whole ecosystem.  

At  this  stage,  the  TES  approach  presents  a  limited  explanatory  power  also  on  the  series  of  industrial 
production  linkages  distributed  along  an  entire  value  chain.  Although  some  considerations  about  strategic 
control of the networks of production in a certain domain can be drawn from specific technological aspects, 
TES does not rely on information about the relative position of players within existing value chains, nor does it 
include information on products and their features (i.e. primary input, intermediate, final) or market structure. 
In this regard, TES is not suitable to directly capture market-dynamics or assess players’ market shares.  

Finally,  complex  techno-economic  environments  are  also  shaped  through  the  dynamic  interaction  of  their 
elements  and  features  with  what  can  be  defined  as  the  ‘rules  of  the  game’  –  that  is,  the  convergence  of 
institutions, regulations, culture, infrastructure that  concur to shape the governance, functions and structure 
of  an  ecosystems.  The  TES  analytical  approach  is  not  designed  to  directly  look  at  and  to  account  for  these 
‘rules of the game’, but acknowledges that these play a role in shaping the features, structure and functions 
of an ecosystem.  

16 

 
 
3  DGTES: applying the TES methodological approach to the digital 

ecosystem  

The Digital Techno-Economic ecoSystem (DGTES) is the application of the Techno-Economic ecoSystem (TES) 
methodology to the analysis digital ecosystem. This section provides an overview of the DGTES workflow. The 
first subsection explains how the technological perimeter of the digital ecosystem has been delimited through 
the  identification  of  a  series  of  relevant  techno-economic  subdomains,  defined  as  digital  areas.  The 
identification of these digital areas allows the construction of the list of keywords then used to build queries 
to  generate  the  DGTES  database  of  the  digital  ecosystem.  The  second  subsection  describes  the  phases  of 
data collection and data processing leading to the generation of the DGTES database. 

3.1  The technological boundaries of the digital ecosystem: digital areas 

Setting the technological boundaries is a necessary exercise to define what belongs to and who participates in 
an ecosystem and, therefore, to provide an adequate overview. The TES methodology comes with a defined 
and reproducible approach to establish the boundaries of any techno-economic ecosystem.  

In  the  specific  case  of  the  digital  ecosystem,  boundaries  were  identified  in  terms  of  main  technological 
subdomains  defined  by  a  set  of  digital  areas.  Digital  areas  correspond  to  informal,  techno-economic 
categories  used  to  cluster  digital  technologies  that  relate  to  the  same  technological  domain  (e.g.  “Artificial 
Intelligence” can be defined as a digital area, entailing various technological solutions based on the principles 
of  artificial  intelligence,  such  as  Natural  Language  Processing  (NLP),  machine  learning,  etc.)  and/or  to  the 
same type of application or final use (e.g. eHealth or eGovernment solutions). The digital areas considered by 
DGTES are complaint with the two criteria of being forward looking and policy relevant. 

This section contains a short description about: (i) why the notion of digital area has been introduced; (ii) what 
are  digital  areas;  (iii)  how  digital  areas  were  identified;  (iv)  how  digital  areas  serve,  in  practice,  the 
implementation of DGTES. 

3.1.1  Why the notion of digital area  

Defining the scope of the term "digital" is challenging. Given the horizontal and transversal nature of digital 
technologies, the vastness of the technological applications that such a concept may include – even starting 
from  a  standard,  conservative  definition  such  as:  electronic  tools,  systems,  devices  and  resources  that 
generate, store or process data - makes it difficult to set boundaries that are at the same time not too broad, 
entailing any activity involving some degree of ‘digital’, and not too restrictive, embracing only very specific 
digital solutions.  

The  notion  of  digital  area  has  been  introduced,  at  this  level,  to  systematise  the  application  of  criteria  to 
tighten the scope of the digital ecosystem, so to avoid the inclusion of devices and tools that are associated 
with more  conventional and  mature  digital solutions now seen more as commodities  (i.e. terms as personal 
computer,  printer,  scanner  and  the  like),  which  would  have  brought  the  whole  exercise  out  of  its  looked-for 
boundaries. Hence, the notion of digital area primarily responds to the need of “digital scoping” in order to set 
clear boundaries to the digital ecosystem. Digital areas are by construction mutually exclusive, in line with the 
aim of  reducing as much as  possible the rise of  possible  ambiguities and of  minimising the return of false 
positive results (even within the same global technological domain).  

At  the  same  time,  the  notion  of  digital  area  allows  restraining  the  perimeter  of  the  digital  ecosystem  to 
technological  subdomains  of  particular  industrial  and  policy  relevance.  In  fact,  a  first  step  to  establish  the 
digital areas was deciding to consider only forward looking and policy-relevant digital subdomains. In practice, 
these  subdomains  are  the  ones  related  to  digital  transformation,  defined  as  the  integration  of  digital 
technology into different market and non-market activities and processes, fundamentally changing the way to 
operate,  produce  and  deliver  value.  Thus,  in  order  to  be  identified  as  a  digital  area  in  DGTES,  a  digital 
subdomain should - in addition to displaying a certain degree of internal technological coherence -  contribute 
to  digital  transformation,  which  becomes  the  guiding  principle  in  setting  the  perimeter  of  the  digital 
ecosystem. Examples of forward looking and policy relevant digital areas include subdomains having a direct 
impact  on  future  infrastructure  (“Electronics,  Semiconductors,  Power  Electronics”,  “Infrastructure,  Cloud 
(Software-as-a-Service),  PaaS 
Computing,  Digital  Platform, 

(Infrastructure-as-a-Service),  SaaS 

IaaS 

17 

(Platform-as-a-Service),  on-line  platforms,  Social  Networks,  Internet”)  as  well  as  technologies  undergoing  a 
continuous  development  concurring  to  the  launch  of  new  market/non-market  possibilities  (“Quantum 
technologies”) or the advance of relatively new ones (“Internet of Things”) 10.  

This  approach  arose  from  extended  consultations  with  stakeholders  and  experts,  and  resulted  in  the 
application  of  a  main  general  criteria  to  concentrate  on  the  front-end  part  and  most  dynamic  digital 
technologies,  disregarding  mature  and  consolidated  manufacturing  processes  with  relative  low  level  of 
innovativeness. 

3.1.2  What is a digital area  

Digital  area  is  an  informal  notion  used  to  group  keywords  that  are  semantically  relevant  to  the  same 
technological domain in a theoretical taxonomy (e.g. “Artificial Intelligence” may be defined as the digital area 
including the following keywords: Natural Language Processing, Decision Support Systems, Machine Learning, 
Deep Learning, Planning, etc.11).  

In order to better understand the definition of digital area, it may be useful to remark the distinction from the 
notion  of  keyword.  A  keyword  corresponds  to  a  technology,  tool,  process,  or  a  characterising  element, 
expressed  as  a  word  or  phrase,  identifying,  or  conceptually  included  in,  one  or  more  digital  areas  (e.g.  the 
aforementioned  Natural  Language  Processing  is  a  keyword  belonging  to  the  digital  area  of  ”Artificial 
Intelligence”).  Thus,  in  the  presented  methodology  each  digital  area  encompasses  a  set  of  related 
technologies that are associated to one or more keywords.  

Although some of those technologies (keywords) could be better specified (e.g.: Natural Language Processing 
may include Computational Linguistics, Machine Translation, Parsing, etc.), the methodological assumption to 
maintain  a  ‘flat  taxonomy’  composed  by  only  two  layers  implies  that  all  the  items  falling  under  “Artificial 
Intelligence” were kept (as keywords) at the same level (i.e., although Parsing would conceptually stay under 
Natural Language  Processing, in our system Parsing and  Natural Language  Processing are two keywords at 
the same level). 

3.1.3  How digital areas were identified 

Digital areas were identified through a series of sequential and iterative phases and steps. These phases and 
steps  were  conducted  with  the  goal  of  progressively  refining  results,  in  order  to  obtain  a  final  number  of 
internally  coherent  and  techno-economically  relevant  digital  areas,  and  to  associate  each  of  these  digital 
areas with a specific list of unique keywords. It is useful to reiterate that the perimeter and scope that has 
been set for the identification of the digital areas is that of digital transformation, defined as the integration 
of digital technology into different market and non-market activities and processes, fundamentally changing 
the  way  to  operate,  produce  and  deliver  value.  Box  2  describes  in  brief  these  sequential  phases  and  steps, 
clarifying how they led to the identification of 15 forward-looking and policy relevant digital areas.  

This  process  of  identification  of  the  relevant  digital  areas  involved  the  application  of  both  manual  and 
automated search on textual documents to  progressively refine results and to reduce  the number of digital 
areas. These iterative refinements were carried out until obtaining a final set of digital areas that both covers 
the highest number of digital subdomains falling within the digital ecosystem and allows to avoid redundant 
areas, by either redistributing keywords or grouping  digital areas referring to the same (or a similar)  digital 
domain. The final output of this whole procedure was a set of 15 digital areas (Table 1).  

(10)   The final list of digital areas is displayed in Table 2.  
(11)   In  the  following,  digital  areas  will  be  expressed  in  quotation  marks  (“Artificial  Intelligence”)  and  keywords  in  italics  (Natural 

Language Processing).  

18 

                                                        
 
Box 2. Identifying the digital areas 

This process of identification of the digital areas starts with a first detection of relevant digital areas through 
an  initial  search  in  selected  relevant  documents,  such  as  reports  or  publications  from  different  sources 
(including  DG  GROW  repository,  JRC  repository,  DG  CNECT  –  Horizon  Europe  Research  and  Innovation 
repository,  and  other  sources)12.  All  the  documents  that  were  analysed  in  this  phase  regard  the  design  and 
implementation  of  sector-specific  policies  that  deal  with  the  definition  of  technologies  (in  terms  of  EU 
positioning vs. other macro-regions, current policies, design of future programmes, etc.), belonging to specific 
sectors (e.g., agriculture, energy, environment, health, etc.) 

Once a first tentative set of digital areas has been identified, a more accurate manual search was performed. 
This manual search consisted of two steps:  

1. Reading carefully the documents, starting from the abstract/introduction to check both the structure of the 
document and whether it was reasonable to assume it to contain issues of interest.  

2.  Searching  for  the  identified  digital  areas  and  implementing  a  naïve  (computational  linguistics-oriented) 
interpretation  of  the  mathematical  notion  of  ‘neighborhood’13.  This  procedure  is  likely  to  detect  more 
keywords  semantically  related  to  that  digital  area  by  setting  the  ‘search  space’  to  a  certain  number  of 
‘neighbouring’ sentences (for example, 3 sentences) every time a phrase containing the term associated to a 
specific digital area (e.g., “Artificial Intelligence”) is  found in a text. This allowed to  make a  further  check in 
order not to skip (possible) keywords to be assigned to a specific digital area.  

After the implementation of  these steps, a  set of 50  digital areas was obtained.  The  aggregation of digital 
areas possibly referring to the same (or similar) digital domain allowed for a reduced 21 entry set (e.g. “3D 
Printing” and “Additive manufacturing”, originally two separate digital areas, were merged into the digital area 
of “3D Printing, Additive manufacturing”)14.  

Then,  one  further  iteration  was  performed  in  order  to  further  reduce  the  number  of  digital  areas  and  to 
eliminate  the  eventual  overlap  of  keywords  across  different  digital  areas.  This  iteration  included  the 
reassignment  of  keywords  from  one  digital  area  to  another  and  the  removal  of  keywords  considered  too 
vague after a second analysis. The final output of this whole procedure was a set of 15 digital areas (Table 
1). 

Table 1. Digital areas in DGTES 

1 

2 

3 

4 

5 

6 

7 

8 

9 

“3D Printing, Additive manufacturing” 

“5G and beyond, Autonomous Networks, Communications, Telecommunications and connectivity” 

“Advanced Computing, High Performance Computing (HPC), edge computing” 

“Artificial Intelligence” 

“Autonomous Systems, Robotics” 

“Blockchain, Distributed Ledger” 

“Cybersecurity, Safety & Security, Digital Identity” 

“Data, Dynamic Data” 

“eBusiness, e-Commerce” 

10 

“Electronics, Semiconductors, Power Electronics” 

11 

“Extended Reality, Virtual Reality, Augmented reality” 

12 

“Infrastructure, Cloud Computing, Digital Platform, IaaS, SaaS, PaaS, on-line platforms, Social “Networks, 

(12)   See Appendix 1 for the list of considered documents.  
(13)   In topology, a neighbourhood is one of the basic concepts in a topological space. It is closely related to the concepts of open set and 
interior. Intuitively speaking, a neighbourhood of a point is a set of points containing that point where one can move some amount 
in any direction away from that point without leaving the set. 

(14)   At this stage, no curation was performed on the list of keywords associated to each identified digital area, except for the deletion of 

duplicates (including for keywords exactly matching the digital areas they belonged to). 

19 

                                                        
 
Internet” 

13 

“Internet of Things, AIDC (automatic identification and data capture)” 

14 

“Quantum Technologies” 

15 
(*) 

“Verticals” (*) 

The “Verticals” area has been added in order to include keywords that refer to final applications rather than to technologies, and 
keywords  that  conceptually  seem  to  belong  more  appropriately  to  other  industrial  ecosystems  (e.g.  Transportations,  Healthcare, 
etc).,  although  still  maintaining  a  link  to  the  digital  one.  One  example  is  the  keyword  health  data  sharing:  despite  obviously 
belonging to the healthcare ecosystem, it anyway maintains a strong link to the digital ecosystem (besides looking more like a class 
of final applications rather than a technology). 

Source: Authors’ elaboration, Digital Economy Unit, JRC. 

3.1.4  How digital areas serve the implementation of DGTES 

The procedure for the identification of the digital areas also allowed associating each of the 15 digital areas 
with a list of unique keywords. Digital areas have been indeed used both in a top-down and bottom-up way. 
First,  during  the  manual  keyword  selection,  digital  areas  facilitated  the  identification  of  more  keywords 
through the  search into better-specified  domain literature and sources (e.g.,  by taking  Artificial Intelligence-
related  sources  and  extract  keywords  only  from  selected  publications).  Then,  by  building  specific  queries, 
digital areas were used to minimize the level of ambiguity many keywords inherently have (e.g., in the query, 
by  accompanying  the  keyword  common-sense  reasoning  with  the  digital  area  “Artificial  Intelligence”,  so  to 
avoid results coming from, say, psychology or social sciences sources).  

Hence, the described methodology allowed obtaining a comprehensive dictionary of keywords associated to 
the domain of digital technologies, focusing on their policy-relevant and forward-looking segments. The final 
list  of  keywords  emerging  from  this  iterative  procedure  was  also  functional  to  identify  the  digital  areas 
defining the boundaries of the digital ecosystem. In fact, the identification of digital areas was driven by the 
progressive refinement of the associated list of keywords but, at the same time, it was also functional to the 
detection  of  relevant  keywords,  which  allow  mapping  the  digital  ecosystem  in  a  forward-looking  and  policy 
relevant perspective 

The 15 digital areas are associated to the final, consolidated list of keywords 15 that constitutes the ‘semantic 
space’ characterizing the digital ecosystem. As described with more details in the next subsection (see Box 2), 
this  list  of  keywords  is  used  to  build  queries  that  are  then  launched  on  the  database  made  up  by  specific 
repositories  (patents,  journals,  business  activities,  EU  funds,  etc.),  in  order  to  generate  the  DGTES  digital 
ecosystem database.  

The currently applied list includes 466 keywords 16, each of which belongs to one of the identified 15 digital 
areas.  These  keywords  allow  to  cover  the  many  facets  of  the  digital  ecosystem  in  a  forward-looking  policy 
perspective, i.e. focusing on technologies and domains that are strictly related to the aforementioned notion 
of  digital  transformation.  At  the  same  time,  these  keywords  allow  to  draw  proper  digital  ecosystem’s 
boundaries, in the sense of not allowing for the inclusion of trivial and too general keywords (e.g. keyboard, 
computer  science)  that  would  dramatically  increase  the  number  of  false  positive  results,  which  are  not 
relevant for the proposed analysis. 

3.2  The DGTES ecosystem database 

The  application  of  the  TES  methodology  to  the  digital  ecosystem  (DGTES)  leads  to  the  generation  of  the 
DGTES  digital  ecosystem  database  of  players  and  activities  engaging  with  digital  technologies.  Using  the 
detailed  information  contained  in  the  DGTES  database,  the  activities  and  the  players  relevant  for  the 
definition of digital ecosystem can be identified and related, taking into consideration features and attributes 
such as organizational type, digital areas, geographical location, etc.. The connection of all these elements and 
information allows mapping the digital ecosystem.   

(15)   The  complete  list  of  keywords  and  queries  is  publicly  available  and  accessible  from  the  European  Data  Portal  and  from  the  JRC 
(https://joint-research-centre.ec.europa.eu/predict/digital-ecosystem-analysis-dgtes-2022/dgtes-keywords-list-

PREDICT  website 
2022_en).  

(16)   While the mentioned set of keywords has been used to define the DGTES environment and build the current database, activities are 
being carried on to extend it to a finer level of granularity, in accordance to the TES methodology, to ensure an even more precise 
capturing of relevant subdomains of the digital technologies. 

20 

                                                        
 
The methodological approach employed to generate the DGTES database unfolds in three phases: (i) detecting 
relevant activities, (ii) identifying players involved in relevant activities, and (iii) connecting these elements to 
generate the DGTES graph database to map the digital ecosystem. 

3.2.1  Detecting activities leaving a ‘digital footprint’ 

The first phase of the DGTES methodological approach consists of the identification, collection and text pre-
processing of the data associated with activities.  

To be included in DGTES, an activity should correspond to an economic process leading to outputs contributing 
to  the  production,  innovation  and  evolution  of  digital  technologies  -  that  is,  increasing  and  expanding  the 
availability  of  and/or  the  knowledge  on  these  technologies.  In  other  words,  activities  should  leave  a  ‘digital 
footprint’  in  one  or  more  of  the  digital  areas  that  define  the  digital  ecosystem.  These  selected  activities 
represent the first main element (or building block) of the DGTES analytical approach.  

With  the  intention  to  establish  a  comprehensive  digital  landscape,  the  DGTES  methodology  targets  three 
different types of activities, organized in three groups according to their nature and outputs (i.e. industrial or 
research). These constitute the ‘three pillars’ sustaining the structure of the digital ecosystem (see De Prato et 
al. 2019, Righi et al. 2020):  

1.  business  activities,  derived  from  information  on  companies’  core  business  and  on  the  production, 
supply  and/or  exchange  of  goods  and/or  services,  and/or  on  investments  and  funds  financing 
industrial and business initiatives (i.e. venture capital deals);  

2. 

innovation  activities,  corresponding  to  outputs  of  Research  &  Development  (R&D)  activities  in  the 
form of patenting initiatives (i.e. filing of priority patents) and/or participation in innovative research 
projects (i.e. EU-funded projects H2020 and FP7);  

3. 

research activities, reflecting academic contributions of frontier research, such as publications and/or 
participation in high level international conferences.  

In practice, each activity is associated to a document that carries textual information about the activity itself 
(i.e.  whether  business,  innovation  or  research)  and  its  attributes.  In  order  to  discern  which  activities  can  be 
considered as having this ‘digital footprint’, the DGTES methodology uses a dictionary of selected keywords17 
to search through different sources of factual data, each source representing a repository of documents that 
carry  textual  information18.  In  the  current  version  of  DGTES,  the  list  of  factual  data  sources  used  includes: 
patent  application  records;  databases  of  EU  funded  projects;  business  information;  databases  of  scientific 
publications and participation in international conferences19. On this textual information a keyword extraction 
algorithm  (using  queries)  is  implemented  in  order  to  detect  the  documents  associated  to  digitally-relevant 
activities.  The  version  of  the  DGTES  presented  in  this  report  uses  466  keywords  and  six  horizontal  data 
sources20. Box 3 clarifies how, in practice, the selected keywords were used to build the queries leading to the 
identification of relevant documents.  

Box 3. Generating the final DGTES database: from keywords to queries 

The keywords (identified through the process explained in subsection 3.1.3) represent the core of the queries 
to be used in the search exercise to build the DGTES digital ecosystem database.  

(17)   The  construction  of  the  keywords  has  been  described  in  the  previous  subsection.  The  complete  list  of  keywords  and  queries  is 
publicly  available  and  accessible  from  the  European  Data  Portal  and  from  the  JRC  PREDICT  website  (https://joint-research-
centre.ec.europa.eu/predict/digital-ecosystem-analysis-dgtes-2022/dgtes-keywords-list-2022_en).  

(18)   The data sources can be of two types: i) horizontal, which entail non-technology specific data sources, but data potentially useful to 
detect  all  type  of  digitally-relevant  activities  (i.e.  firm  level  information  repositories  containing  data  on  any  type  of  firms  in  all 
industrial sectors); ii) vertical, which correspond to technology specific data sources, such as information exclusively related to, in 
the current case, digital technologies (i.e. data collected by industrial association targeting a specific technological domain, or, for 
example lists of start-ups active in artificial intelligence, robotics, etc.).  

(19)   This list could be expanded in future versions of DGTES.  
(20)   Work  is  ongoing  to  include  in  the  source  list  a  set  of  vertical  sources,  specific  to  the  digital  domain  and  to  the  identified  digital 

areas. 

21 

                                                        
 
The  construction  of  queries  must  follow  some  rules,  especially  aimed  at  avoiding  as  much  as  possible  the 
return of false positive results that are not relevant for the purpose of this analysis. This effect is mainly due 
to the fact that many keywords, taken in isolation, maintain a high degree of ambiguity. For example, when 
taken  in  isolation,  the  term  5G  may  correspond  to  the  “5th  generation  technology  standard  for  broadband 
cellular networks” but also to the phrase “five grams”; decision tree may refer (among others) to psychology 
or project management or, within the digital ecosystem, to Artificial intelligence (AI).  

The first step to build  queries from the set of keywords,  then, is establishing whether a keyword  has  some 
level of ambiguity or not. For instance, while it is difficult to imagine a context where cybersecurity refers to 
some context which is not inherently digital; the same cannot be said for fraud, or grid, or inference. So, as far 
as the actual construction of queries is concerned, if one keyword is labelled as unambiguous, then the query 
can be simply built with the keyword itself. One example is the query “chatbot”, made up by just the keyword 
chatbot. 

Digital areas may be used in order to minimise the noise (and false positive results) deriving from keywords’ 
ambiguity. When one keyword is labelled as ambiguous, then the query can be built by putting the digital area 
and the keyword in an AND relation, like this: (digital area AND keyword). For instance, the keyword fraud will 
concur  to  the  construction  of  the  query  (Blockchain,  Distributed  Ledger  AND  fraud);  the  keyword  grid  will 
concur to build the query (Advanced Computing, High Performance Computing, edge computing AND grid). 

When digital areas are constituted by several elements, as the ones used in the examples above, real queries 
will use the OR operator in order to widen their search potentialities. So, the example above should actually be 
written  as  (("Advanced  Computing"  OR  "High  Performance  Computing"  OR  "Edge  computing")  AND  ("Grid")). 
Quotes (“) are inserted for all the constituents of the query, especially in order to isolate phrases made up by 
more than one word. 

Digital  areas  cannot  always  be  used  as  they  are.  Most  of  them  are  composed  by  constituents  made  up  by 
multiple  words  or  phrases  (“3D  Printing,  Additive  manufacturing”).  The  first  question  to  take  into  account  is 
whether  such  constituents  are  ambiguous  or  unambiguous  themselves  (in  the  framework  of  the  digital 
ecosystem). For instance, while “cybersecurity” may be considered unambiguous, the same cannot be said for 
“infrastructure”. 

In order to minimize the risk to return results from other semantic contexts, two basic rules should be applied: 

1.  In case a digital area contains more than one element, and those elements are totally unambiguous (e.g., 
“3D Printing, Additive manufacturing”), those elements can be simply put in OR; 

2.  In  case  a  digital  area  contains  one  or  more  elements  inherently  ambiguous  (e.g.,  “data”,  “electronics”, 
“infrastructure”), then we may decide either to: 

—  merge  (in  AND)  two  or  more  elements  belonging  to  the  same  digital  area  (e.g.:  “5G”  AND 

“communications”); or 

—  “specify”  the  element  by  completing  it  with  one  (or  more)  additional  element  that  better  specify  it 
(e.g., “infrastructure” becoming “software infrastructure”, or the like), paying attention that the added 
element is not ambiguous in its own. 

The  ambiguous/unambiguous  labelling  of  keywords  and  the  proper  use  of  the  AND/OR  Boolean  operators 
make  it  possible  to  build  queries  that  allow  at  the  same  time  a  high  representational  power  and  the 
minimization of false positive results. 

As the scope of the analysis is to obtain an updated picture of the digital ecosystem, the detected documents 
are  filtered  based  on  the  year  of  reference  and  only  documents  issued  between  2009  and  2022  are 
considered21.  The  inclusion  of  the  latest  years  allows  capturing  the  most  recent  evolution  of  digital 
technologies in a timely manner. The possibility to associate each document to a year is an important feature 
of the DGTES, as it allows analysing the evolution of the digital ecosystem over time and the emergence of 
subdomains of digital technologies.  

(21)   For  some  documents  -  in  particular  those  containing  the  description  of  a  firm’s  activities  and  business  -  a  date  is  possibly  not 
available.  When the  data  sources  provide  information  about economic  activities  currently existing,  the  year of  reference  is  taken 
depending  on  a  systematic  set  of  considerations  to  proxy  the  year/s  in  which  relevant  activities  may  have  been  performed, 
assuming that the information was valid at that time. 

22 

                                                        
 
3.2.2 

Identifying the players shaping the digital ecosystem 

The  second  phase  of  the  DGTES  methodological  approach  consists  of  the  identification  of  the  players 
participating in the digital ecosystem.  

Economic players represent the second main element (or building block) of the DGTES analytical approach. In 
DGTES, the term ‘economic player’ is used to define any economic (market and non-market) actor involved in 
digitally-relevant  activities.  These  ‘behaving  entities’  (Righi  et  al.  2020)  play  an  active  role  in  shaping  the 
digital ecosystem and influencing its economic performance, development and future evolution.  

Players can be:  

1.  companies and firm;  

1.  academic institutions and research centres;  

2.  governmental authorities and bodies.  

This classification recalls the ‘triple helix’ model of innovation developed to describe the interactions between 
the main actors engaging in innovation activities and pursuing innovation outputs: university, government, and 
industry  (Etzkowitz  and  Leydesdorff  1995;  2000).  Although  the  three  entities  may  perform  innovation  in 
response to different incentives and with different purposes (i.e. industry responds to market incentives and 
develops innovation to boost competitiveness, while university engages in research for non-market purposes), 
the  ‘triple  helix’  model  contemplates  a  possible  evolution  towards  more  hybrid  frameworks,  where  each 
component  evolves  to  adopt  some  characteristics  of  the  other  institution.  The  fact  that  DGTES  does  not 
impose a univocal correspondence between the type of activity and the type of player – that is, each player 
can perform one or more business, innovation and/or research activities - makes this approach adequate to 
account for the possible hybrid nature of players.  

The  DGTES  methodology  identifies  the  players  through  the  analysis  of  the  textual  information  contained  in 
the  document  associated  to  a  digitally-relevant  activity22.  However,  this  requires  the  implementation  of  a 
multi-steps process of data cleansing before the database is ready and usable for the analysis of the digital 
ecosystem. Box 4 summaries this multi-step process.  

Box 4. Generating the final DGTES database: from documents to players  

A  data  cleansing  process  is  performed  on  the  selection  of  documents  resulting  from  the  analysis  of  their 
textual information, in order to improve precision for the identification of unambiguous, relevant players. This 
process consists of the following steps:  

- Look for information on players and players’ location 

Only documents containing text information about players are considered. Where applicable, players’ locations 
(at  the  city  level)  are  corrected  or  completed  based  on  information  contained  in  the  document,  via  web 
scraping, or from other sources.  

-  Disambiguation of players 

Players’  names  need  to  be  disambiguated,  as  the  same  player  can  be  simultaneously  detected  by  multiple 
factual  data  sources.  The  process  of  disambiguation  allows  identifying  if  the  same  player  is  involved  in 
multiple  activities  and  correcting  for  eventual  duplications.  This  process  is  based  on:  (i)  the  geographical 
location  of  players  (eliminating  players  with  the  same  name  that  share  the  same  location  at  city  level);  (ii) 
algorithms measuring similarity of names that identifies misspelled names and variations of the same name; 
(iii)  algorithm  working  on  network  of  similarities  that  identifies  groups  of  potential  duplicated  agents.  This 
implies that the information on each player can result from the combination of what collected from different 
sources. If the same economic institution is detected in multiple locations, these are considered as different, 
individual  players,  belonging,  when  applicable,  to  the  same  multi-site  organisation  (i.e.  firms  with  an 
headquarter and several subsidiaries).  

(22)   This implies that business activities that do not explicitly include the production, supply and/or exchange of digital technologies in 
the description of their core business are not considered. Nevertheless, they can be detected if they have filed a digitally-relevant 
patent  or  have  a  digitally-relevant  publication  (Righi  et  al.  2020)  or  having  received  venture  capital  (VC)  funding  for  a  digitally-
relevant activity, and so on. 

23 

                                                        
 
- Identify the organisational type  

The  organisational  type  of  each  player  is  identified,  which  can  correspond  to:  academic  institutions  and 
research  centres;  governmental  authorities  and  bodies;  firms  and  companies.  The  focus  is  placed  on  the 
organisations,  and  not  on  individuals  (i.e.  the  applicant  organisation  owning  the  invention  in  the  case  of 
patents, or affiliation instead of the individual authors in conference proceedings or publications). 

3.2.3  Generating the graph database to map the digital ecosystem 

The  third  phase  of  the  DGTES  methodological  approach  generates  the  graph  database  of  the  digital 
ecosystem, built on the interlinkages connecting activities and players. 

The database obtained through the reiteration of keywords queries and the processes of data cleansing and 
disambiguation contains detailed information about the participation of each player in the detected activities 
and  about  their  features  and  attributes.  More  specifically,  the  DGTES  methodology  uses  on  two  sets  of 
information to generate the graph database and map the digital ecosystem:  

1. 

2. 

information  about  the  detected  activity,  such  as  the  description  of  the  activity  (e.g.  from  textual 
information  in  documents  such  as  the  description  of  companies’  activity,  abstract  of  articles  or  of 
patents),  the  date  of  the  activity  (publication  date,  patent  application  filing  date…),  and  the  digital 
area associated to the activity;  

information  about  the  related  players,  such  as  the  organizational  type  (e.g.  firm,  government 
institution,  research  institution…),  geographical  location,  industrial  sector  and  financial  information 
(when applicable).  

The  analysis  of  the  textual  information  in  the  documents  allows  connecting  activities  and  players,  thus 
identifying  how  players  relate  to  each  other  and  are  interlinked  through  their  shared  activities.  The  web  of 
relationships  built  on  the  connections  across  activities  and  players  constitute  the  structure  of  the  DGTES 
digital ecosystem graph database.  

The final graph database reflects a complex system made of players, activities and their respective attributes. 
This  system  is  populated  by  interdependent  agents  whose  (formal  or  informal)  connections,  interactions  on 
joint activities, and behaviours reveal the emergence of a non-random structure (Newman 2011). The graph 
structure of the DGTES digital ecosystem database allows it to be explored as a network23, whose nodes and 
edges correspond to players and activities.  

The  fact  that  the  data  are  collected  at  a  micro-level  from  a  heterogeneous  group  of  factual  data  sources 
allows enriching the scope of the mapping exercise with more and finer levels of analysis. The possibility to 
retrieve  agents’  locations  at  high  granularity  (e.g.  city  level)  allows  exploring  the  ecosystem  at  different 
geographic  and  administrative  levels  (e.g.,  cities,  countries,  regions).  The  spatial  dimension  is  indeed  a 
fundamental and original feature of the TES methodology: based on the information on their location, players 
belonging to the same geographical area can be grouped together to produce their geo-based networks and 
explore  differences  and  their  relative  positioning  at  specific  geographic  levels  as  well  as  at  global  level. 
Following  this  same  approach,  DGTES  can  map  the  geographical  space  of  production  and  diffusion  of 
knowledge around digital technologies.  

In addition, the information on digital areas allows relating individual players to digital subdomains. Besides 
offering useful insights on which players are contributing to the evolution of specific digital technologies, this 
allows looking at the  composition of each digital  subdomain in terms of the  main attributes of players (i.e. 
organisational  type,  geographical  location,  etc.)  and  exploring  the  distribution  of  digital  capabilities  and 
technological competences in the ecosystem. This feature of DGTES is particularly powerful and useful to map 
knowledge flows based on interlinkages across players and colocation at local as well as at global level, and 
to investigate collaborations and the evolution of emerging domains in digital technologies. 

Moreover, a further analysis of the textual information in the documents allows to identify in each document 
a  specific  set  of  technology-related  terms.  These  terms  can  be  pegged  to  the  activity  associated  to  the 
document  and  stored  as  its  additional  attributes.  In  this  way,  the  connection  of  each  activity  with  a  set  of 
terms constitute another layer of relations among activities – and, therefore, also among the related players – 
that enriches the complexity of the network underlying the digital ecosystem. This allows performing bottom 

(23)   See Righi et al. (2020) for more details on how the graph database can generate different types of networks.  

24 

                                                        
 
up  topic  modelling  of  thematic  areas,  which  increases  the  explanatory  power  of  DGTES  and  its  potential  in 
revealing latent communities of players as well as relations across digital subdomains.  

Considering the volume and variety of sources considered, and the amount of work needed to match through 
the data sources, the TES analytical approach provides one of the most accurate and comprehensive way to 
describe, map and analyse a dynamic and complex ecosystem of emerging technologies (De Prato et al. 2019; 
Pogorzelska et al. 2019; Samoili et al. 2019; Righi et al. 2020). The implementation of DGTES shows that this 
is the case also for digital technologies and the digital ecosystem. 

To  summarize,  Figure  2  presents  a  schematic  representation  of  the  workflow  of  the  DGTES  analytical 
approach described in this section. It displays the main aspects of the DGTES methodology leading to a map 
of the digital ecosystems, going from the detection of the activities characterising the digital ecosystem (left 
part) to the procedures to cleanse the data, to the production of the final graph database (right part).  

Figure 2. DGTES workflow and methodology 

Source: Authors’ elaboration, Digital Economy Unit, JRC. 

25 

 
4  DGTES at work: mapping and navigating through the digital ecosystem  

This  section  provides  an  overview  of  the  possible  outcomes  of  the  application  of  the  Techno-Economic 
ecoSystems (TES) approach to the study of the digital ecosystem (DGTES). The first subsection suggests some 
paths of analysis that can be explored using the  DGTES  digital ecosystem database.  The second subsection 
introduces some of the indicators and metrics that can  be generated from the  DGTES database and  briefly 
discusses how these can provide a first overview of the digital ecosystem. The third subsection builds on the 
presented indicators to  point out future  directions of analysis, stressing the potential  of DGTES in exploring 
further dimensions and aspects of the digital ecosystem, and beyond it. 

4.1  An itinerary to explore the digital ecosystem  

The  aim  of  DGTES  is  to  map  the  digital  ecosystem  and  provide  an  analysis  of  its  elements,  structure  and 
features  from  a  holistic,  multidimensional  and  policy-relevant  perspective.  In  particular,  this  descriptive  and 
analytical exercise can address three sets of research questions:  

1.  Which  are  the  economic  players  participating  in  the  supply,  creation  and  evolution  of  digital 
technologies, by engaging in relevant research, innovation and  business activities? Where are they 
located and how are they geographically distributed?  

2.  Which  technological  subdomains  (digital  areas)  define  the  boundaries  of  the  digital  ecosystem? 
Which  are  the  technological  realm  of  the  activities  of  the  players  in  the  digital  ecosystem?  Which 
digital areas represent emerging technological subdomains? 

3.  How do players interact in the digital ecosystem? Which network of collaborations and interlinkages 
results from their shared activities, locations and technological fields? Which type of collaborations 
are in place in the digital ecosystem? Which players are in the strategic position to control and shape 
the flows of knowledge and production? 

These research questions can serve as research paths to navigate through the digital ecosystem with a clear 
analytical focus and to grasp a deeper and meaningful understanding of its elements, structure and features.  

From a policy perspective, this research exercise can offer novel evidence on the positioning of the European 
Union  (EU)  in  terms  of  attractiveness  and  excellence  in  research  and  innovation  (R&I)  and  in  terms  of 
industrial leadership and competitiveness in the domain of emerging and critical digital technologies. This can 
contribute to the design of policies that help Europe successfully navigate the digital transition. By shedding 
light  on  the  networks  that  characterize  the  global  digital  landscape,  the  results  of  this  research  can  also 
contribute to the identification of opportunities, synergies, and obstacles that need to be addressed to foster 
the  development  of  a  vibrant  digital  ecosystem  and  to  strengthen  EU’s  digital  and  industrial  leadership  in 
current and future global scenarios. 

4.2  DGTES indicators and metrics 

Offering  a  quantitative  response  to  the  issue  of  measuring  and  assessing  dynamic  techno-economic 
ecosystems  in  the  absence  of  adequate  conventional  statistics,  the  application  of  the  TES  methodological 
approach  allows  generating  a  series  of  indicators  that  can  help  disclose,  investigate  and  better  understand 
the digital ecosystem.  

This subsection introduces and discusses some of the indicators and metrics that can be produced from the 
DGTES  database.  These  can  be  grouped  in  different  sets  according  to  their  goals  and  to  dimensions  of 
analysis:  (i)  indicators  that  provide  an  overview  of  the  digital  ecosystem  global  landscape,  with  its  main 
elements  and  dimensions  of  analysis;  (ii)  indicators  that  describe  and  analyse  the  network  nature  of  the 
digital ecosystem. 

The results of the calculations of these indicators and metrics will be presented and discussed in an upcoming 
work  based  on  the  DGTES  database  obtained  considering  the  period  between  2009  and  2022.  Most  of  the 
presented  indicators  can  also  be  calculated  for  each  year  in  the  covered  period,  thus  allowing  to  add  a 
longitudinal  and  time-varying  perspective  to  the  analysis  of  the  digital  ecosystem.  All  these  indicators  and 
metrics represent a first step towards addressing the research questions presented in Section 4.1. 

Building  on  the  economic  literature  that  describes  the  economy  and  economic  activities  as  interconnected 
systems,  the  indicators  produced  with  the  DGTES  analytical  approach  are  inspired  by  theories  of  complex 
systems and apply techniques of network analysis. In this sense, DGETS also complements and push forward 

26 

a  line  of  research  to  which  JRC  is  already  contributing  with  different  scopes  and  objectives,  since  earlier 
exercises  aiming  at  identifying  the  European  Poles  of  ICT  Excellence,  or  analysing  the  worldwide  innovation 
networks  (De  Prato  and  Nepelski  2013;  De  Prato  and  Nepelski  2012),  to  the  current  work  on  economic 
complexity  (Pugliese  and  Tacchella  2021).  However,  this  latter  works  aim  at  measuring  the  export  and 
innovation  capabilities  necessary  for  a  country  or  region  to  be  competitive  also  relying  on  product  data, 
whereas DGTES focuses more on analysing and understanding the dynamics, features and interactions within 
segments of emerging technologies such as digital ones. Moreover, thanks to the granularity of the underlying 
microdata,  DGTES  allows  to  apply  a  different  geographical  focus  (i.e.  global,  country,  regional)  and  even  to 
zoom at the level of players and organizations. 

4.2.1  An overview of the digital global landscape  

Setting the stage: players and activities in the digital ecosystem 

The  analysis  starts  with  the  identification  of  the  main  elements  that  define  the  digital  ecosystem.  In  this 
regard, DGTES can provide a snapshot of the activities (business, innovation, research) taking place within the 
perimeter of the digital ecosystem and of the related players (firm, research institution, government) (Table 2 
Indicators 1.a).  

The  high level of  granularity is a crucial feature of the DGTES database.  Exploiting the full  range of  micro-
data  collected  across  different  data  sources,  DGTES  allows  grasping  a  finer  picture  of  the  elements  of  the 
ecosystem and of their attributes. For example, it allows relating the organizational type of the players with 
their involvement in various types of activities (Table 2 Indicators 1.b). This implies that DGTES can ‘zoom in’ 
and focus on specific actors in the digital ecosystem, such as firms and companies (Table 2 Indicators 1.c.). By 
crossing the available information on firm’s characteristics (i.e. size, main sector of activity), DGTES can offer 
an original and novel look on industrial capacity and composition in the digital ecosystem. 

DGTES can also  provide a dynamic overview of the digital ecosystem. The fact that the DGTES database is 
built  on  microdata  collected  over  a  time  span  of  more  than  10  years  (2009-2022)  makes  it  possible  to 
generate the indicators at different points in time. Adding this longitudinal dimension allows picturing how the 
digital ecosystem has evolved over time, in terms of composition of both players and activities. Picturing the 
evolution of the digital ecosystem can help reveal weak signals of future technological trajectories, supporting 
policymakers with the identification of digital subdomains that can shape future digital leadership. 

Table 2. Indicators 1: Players and activities in the digital ecosystem 

Indicators 1  Questions 

Indicators (*) 

1.a 

What happens in the digital ecosystem?  

Total number of activities 

Which  activities  are  predominant? 
business, innovation or research?) 

(i.e. 

Share of activities by type (over total number 
of activities) 

Who populates the digital ecosystem?  

Total number of players 

Which  type  of  players  are  more  common? 
(i.e. firms, research institutions, government 
institutions?) 

Share  of  players  by  organizational  type  (over 
total number of players) 

1.b 

How do the different players engage in the 
digital ecosystem?  

Which  players  do  what?  Are  some  players 
specialized in some activities? 

type  per  each 
Share  of  activity  by 
organizational  type  (over  total  number  of 
activities  performed  by  each  organizational 
type) 

1.c 

Example: focus on firms 

How  do  firms  engage 
ecosystem?  Which  activities  do 
perform more frequently? 

in  the  digital 
they 

Share of activities performed by firms by type 
(over  total  number  of  activities  performed  by 
all firms) 

(*) 

The indicators can be calculated for each year in the considered period (2009-2022). The same applies to Table 4, 5 and 6.  

Source: Authors’ elaboration, Digital Economy Unit, JRC. 

27 

 
 
 
Adding a first layer to the analysis: geographical distribution of the digital ecosystem 

The information on the location of  players allows including the geographical  dimension into the analysis of 
the  digital  ecosystem.  DGTES  can  provide  a  picture  of  the  geographical  distribution  and  composition  of  the 
digital ecosystem, offering an overview of where the main players are located and how they participate in the 
ecosystem’s activities (Table 3 Indicators 2.a).  

The  granularity  of  the  DGTES  database  allows  highlighting  different  geographical  levels  in  the  digital 
ecosystem  (geographical  area,  country,  regional,  or  city  level).  Based  on  this  information,  geographical 
agglomerations of  players are generated by considering together all players sharing the same  geographical 
area  (even  at  different  levels)  (Table  3  Indicators  2.b).  In  this  way  DGTES  can  show  the  relative  weight  of 
different geographical areas (i.e. countries) in the digital ecosystem. This feature of DGTES is fundamental for 
the comprehension of the spatial dimension of the digital ecosystem.  

By breaking down the geographical composition of the ecosystem also by type of player and activities, DGTES 
makes it possible to zoom in and look at the situation of specific actors in a specific geographical area (Table 
3 Indicators 2.c). For instance, it can display which geographical area hosts the largest share of firms, or in 
which activities these firms are engaging in a given geographical area. Relying on the available information on 
firm  characteristics, it can also look at the  composition of firms’ foreign ownership and relate this with the 
relative weight of the geographical areas of origin of the foreign owner, as it emerges from the analysis of 
the digital ecosystem (Table 3 Indicators 2.d). 

By  crossing  the  information  on  activities,  players  and  their  location,  some  indicators  of  the  intensity  of 
activities can be calculated for each geographical area. The number of activities per player allows for a first 
rough comparison of ‘how productive’ (in terms of activities performed per player) different areas are (Table 3 
Indicators 2.e).  In addition, for  comparative  purposes, the size of the  countries’ economies (proxied by  GDP, 
measured in EUR billion purchasing power standards (PPS) 24) can be used to get an idea of ‘how productive’ a 
geographical area is with respect to its economic weight.  The number of players can also be  related to the 
country GDP (Table 3 Indicators 2.f).  

Calculating these indicators for different years over the period covered by the DGTES database (2009-2022) 
allows  exploring  how  the  geographical  composition  of  the  digital  ecosystem  has  evolved  over  time.  This 
shows how the relative weight of the different geographical areas has changed in terms of ‘critical mass’ of 
players  and  activities  within  the  digital  ecosystem,  thus  revealing  which  geographical  areas  have  engaged 
more in the global digital landscape and/or which areas have been evolving faster.  

Table 3. Indicators 2: Adding the geographical dimension to the digital ecosystem 

Indicators 2  Questions 

Indicators 

2.a 

Where are players located?  

Where do activities take place? 

2.b 

Which  are  the  main  geographical  areas  in 
terms  of  ‘critical  mass’  of  players  and/or 
activities? 

Which  type  of  players  or  activities  are  more 
frequent in a given geographical area? 

Total  number  of  players  by  geographical 
area 

Total  number  of  activities  by  geographical 
area 

Share of players by  geographical area (over 
total number of players) 

Share  of  activities  by  geographical  area 
(over total number of activities) 

Share  of  players  by  organizational  type  per 
each  geographical  area  (over  total  number 
of players in each geographical area) 

Share  of  activities  by 
type  per  each 
geographical  area  (over  total  number  of 

(24)  The  use  of  GDP  values  in  purchasing  power  standards  (PPS)  allows  to  account  for  price  differences  across  countries.  For  more 

information on this indicator, see Righi et al. (2020).    

28 

 
 
                                                        
 
activities in each geographical area) 

Percentile  distribution  of  number  of  players 
in different geographical area 

Example: focus on firms 

2.c 

Where are most firms located? 

Share of firms by geographical area 

How  do 
firms  engage 
ecosystem in each geographical area? 

in 

the  digital 

Total number of firms by geographical area 

Total  number  of  activities  performed  by 
firms by geographical area 

2.d 

2.e 

2.f 

To what extent are firms owned by or related 
to third—country actors? 

Firms’ location and foreign ownership 

How  ‘productive’  is  the  digital  ecosystem  in 
each geographical area? 

Average  number  of  activities  per  player  in 
each geographical area 

How  ‘productive’  is  the  digital  ecosystem  in 
each  geographical  area  with  respect  to  the 
size of its economy? 

Median  number  of  activities  per  player  in 
each geographical area 

Total  number  of  activities  over  country  GDP 
per each geographical area 

Total  number  of  players  over  country  GDP 
per each geographical area 

Source: Authors’ elaboration, Digital Economy Unit, JRC. 

Adding a second layer to the analysis: digital areas and technological composition of the digital ecosystem 

Digital  areas  correspond  to  the  techno-economic  subdomains  that  define  the  boundaries  of  the  digital 
ecosystem  from  a  forward-looking  and  policy  relevant  perspective  (as  described  in  Section  3.1).  The 
information  on  the  digital  areas  allows  adding  a  stronger  technological  focus  to  the  analysis  of  the  digital 
ecosystem, which can, for example, help detect weak signals of emerging technological domains in the global 
digital landscape.  

DGTES can offer an overview of how the digital areas are scattered through the ecosystem and what is their 
relative weight in the digital landscape (Table 4 Indicators 3.a). The number of different digital areas in which 
each  player  engage  can  give  an  idea  of  how  specialized  or  diversified  players  are  in  terms  of  digital 
subdomains of their activities (Table 4Indicators 3.b). In addition, the information on the organizational type 
of the players can provide an even finer picture of the technological composition of the digital ecosystem, for 
instance by revealing how each type of player participates in the digital areas (Table 4 Indicators 3.c).  

Crossing the information on players' location and digital areas allows looking at the geographical distribution 
and composition of the digital areas (Table 4 Indicators 3.d). This offers a first representation of the relative 
participation  of  each  geographical  area  in  the  considered  digital  subdomains,  giving  an  idea  of  the  overall 
geographical  distribution  of  technological  competences  and  capabilities  within  the  digital  ecosystem.  This 
information can be particularly interesting and policy-relevant in the case of digital areas associated to critical 
emerging  digital  subdomains.  This  feature  of  DGTES  is  important  for  a  forward-looking  assessment  of  the 
evolution of the global digital landscape.  

In  this  regard,  a  useful  indicator  of  the  relative  specialization  in  a  certain  digital  area  is  the  revealed 
comparative  advantage  (RCA)  (Righi  et  al.  2021)  (Table  4  Indicators  3.e).  The  RCA  measures  technological 
specialisation by comparing each geographical area’s breakdown of activities in a certain digital area against 
all activities in that digital area worldwide (world average) 25.  

(25)   The revealed comparative advantage (RCA) is calculated as the ratio of the share of activities of a geographical area in a certain 
digital area in the total amount of activities in that geographical area over the share of activities in that digital area worldwide in 
the total amount of activities worldwide. A RCA value of 1 represents the global average specialisation; thus, this value is taken as 

29 

 
 
 
                                                        
 
The  possibility  to  calculate  these  indicators  for  different  years  over  the  covered  period  (2009-2022)  can 
provide a better understanding of the technological dynamics in the digital ecosystem. Changes in the relative 
distribution  of  the  digital  areas  allows  pointing  out  emerging  technological  trajectories,  showing  how  some 
clusters  of  digital  technologies  have  started  arising  and  how  some  others  have  eventually  become 
predominant  over  time,  occupying  a  relatively  larger  weight  within  the  digital  ecosystem.  Moreover,  the 
analysis of the evolution of the types of activities within a digital area can reveal the existence of patterns of 
technological  maturity  (for  instance,  if  in  a  digital  area  the  majority  of  activities  shifts  from  research  to 
innovation or business over time).  

The  information  on  the  geographical  distribution  of  digital  areas  also  allows  relating  the  evolution  of  the 
geographical  and  technological  dimensions  of  the  digital  ecosystem.  By  showing  on  how  the  relative 
involvement in digital subdomains of different geographical areas  has changed over  time, DGTES  can offer 
novel insights on the episodes of technological ‘catching up’ or ‘leapfrogging’ eventually experienced by some 
geographical areas in correspondence to successful reductions of the technology gap (Lee and Malerba 2017). 
Being  the  technology  gap  one  of  the  root  causes  of  strategic  dependencies,  this  exercise  can  help  disclose 
relevant information on the global patterns of emerging technological and industrial leadership.  

Table 4. Indicators 3: Adding the technological dimension to the digital ecosystem 

Indicators 3  Questions 

Indicators 

3.a 

Which  are  the  main  digital  areas  in  the 
ecosystem? 

Total number of activities by digital areas 

Share of activities by digital  areas (over total 
number of activities) 

Total number of players by digital areas 

Share  of  players  by  digital  areas  (over  total 
number of players) 

3.b 

3.c 

3.d 

3.e 

How  specialized  are  players  in  terms  of 
digital areas?  

Average number of different digital areas per 
player 

Median  number  of  different  digital  areas  per 
player 

the  different  players 
type)  engage 

(per 
How  do 
organizational 
in  digital 
areas?    Do  some  type  of  players  engage 
more in some digital areas? 

Share 
digital 
of 
organizational type   

areas 

by 

players’ 

Share of players’ organizational type by digital 
area 

What is the geographical distribution of the 
digital  areas?  To  which  geographical  area 
corresponds  the  largest  share  of  activities 
in each digital area? 

Share of geographical areas by digital area 

Share of digital areas by geographical area 

In  which  digital  areas  is  each  geographical 
area more competitive? 

Revealed comparative advantage (RCA) 

Source: Authors’ elaboration, Digital Economy Unit, JRC. 

4.2.2  A collaborative ecosystem   

A complex system of relations by design 

the benchmark. A value of RCA above 1 for a geographical area in a certain digital area means that the proportion of activities in 
that digital area is higher than in the world average, and therefore reveals a comparative advantage of the considered geographical 
area in that digital area. For more details and examples about the use of this indicator, see Righi et al. (2021).  

30 

                                                                                                                                                                             
 
 
As  discussed  in  Section  3.2,  the  web  of  connections  across  players  based  on  their  shared  activities  and 
locations constitutes the main structure of the DGTES digital ecosystem graph database. This can be explored 
as  a  network  of  interlinkages.  Analysing  this  network  with  adequate  indicators  can  reveal  the  features  of 
collaborations  and  knowledge  flows  within  the  ecosystem,  while  keeping  a  geographical  (worldwide,  EU, 
regional) and technological (digital areas) perspective. This feature of DGTES is crucial for the comprehension 
of  the  complex  structure  of  the  digital  ecosystem  and  of  its  characteristics  in  terms  of  production, 
development, exchange and control of relevant knowledge.  

DGTES  can  provide  a  unique  picture  of  the  volume  and  the  features  of  the  collaborations  that  take  place 
within the digital ecosystem26. It can shed light on how collaborative the digital ecosystem is by looking at the 
amount  of  shared  (thus,  collaborative)  activities  as  well  as  at  which  activities  are  conducted  by  individual 
players (thus, players that operate in isolation from the rest of the ecosystem) (Table 5 Indicators 4.a). The 
granularity  of  the  information  on  players  and  activities  contained  in  the  DGTES  dataset  allows  to  better 
describe  the  patterns  of  collaboration.  For  instance,  it  can  give  an  idea  of  to  what  extent  different 
organizational  types  are  involved  in  the  observed  collaborations  (i.e.  firms  with  universities  and/or  research 
institutions),  while  the  information  on  type  of  activities  can  reveal  which  activities  tend  to  be  more 
collaborative (Table 5 Indicators 4.b).   

Geographical networks of collaborations can be generated by considering together all players from the same 
geographical area. This allows looking at spatial characteristics of these collaborations, such as how spread 
out  collaborative  players  are,  how  collaborative  a  certain  geographical  area  is,  or  what  is  the  volume  of 
internal (when players are located in the same geographical area) or external (when players do not share the 
same location) collaborations (Righi et al. 2020) (Table 5 Indicators 4.c). It is noteworthy that DGTES can also 
highlight ‘latent’ knowledge flows based on implicit relations and (hypothetical) tacit processes of knowledge 
exchange potentially driven by players’ spatial proximity and co-location (Table 6 Indicators 4.d). This indicator 
can  provide  more  insights  on  the  nature  and  purpose  of  implicit  collaboration  patterns  in  the  digital 
ecosystem,  allowing  to  detect  eventual  ‘latent  communities’  and  tosee  to  what  extent  these  overlap  with 
formally observed communities, and even associate them to specific digital domains.  

Additional indicators can be constructed using the information on the digital areas. These allow exploring to 
what extent collaborative players are more or less diversified in terms of the digital areas they engage in, or 
if non-collaborative players tend to specialize in some specific digital areas (Table 5 Indicators 4.e). This can 
provide an indication of possible emerging digital domains, whose knowledge is still unset and evolving, thus 
making players more reluctant to engage in collaborations.  

DGTES fully exploits its explanatory power when it looks more in depth at the properties of the network. By 
revealing  the  presence  of  gatekeepers  and  hubs,  indicators  derived  from  network  analysis  (i.e.  network 
statistics, such as weighted  betweenness centrality (WBC) 27) allow assessing the level of control exerted by 
different groups of players or geographical areas over the network (Righi et al. 2020) (Table 5 Indicators 4.f). 
These  indicators  allow  introducing  an  additional  analytical  level  to  the  study  of  the  digital  ecosystem:  the 
identification of strategic positioning in emerging digital domains.  

As  information  about  time  is  available  for  research  and  innovation  activities  (i.e.  patents,  publications  and 
conferences), these indicators can be computed on a yearly basis, considering only the set of collaborations 
occurring  in  each  year  at  a  time.  When  calculated  at  different  points  in  time,  network  statistical  indicators 
(such  as  WBC)  describe  how  the  control  over  the  knowledge  flow  within  the  digital  ecosystem  has  evolved 
over the considered period (2009-2022). This reveals how the relative positioning of the technological leaders 
within  the  network  have  changed  over  time  and,  at  the  same  time,  pointing  out  the  presence  of  potential 
emerging leaders. This feature of DGTES is particularly relevant from the perspective of strategic autonomy 
emphasised  in  the  EU  Industrial  Strategy,  as  it  can  contribute  to  a  better  understanding  of  the  current  and 
potential  role  of  EU  in  the  digital  ecosystem  as  well  as  to  detecting  possible  vulnerabilities  and  sources  of 
strategic  dependencies  that  could  compromise  EU’s  leadership  in  the  current  and  future  global  digital 
landscape. 

(26)   The  exploration  of  collaborations  within  the  digital  ecosystem  relies  on  research  (i.e.  journal  publications,  conferences)  and 

innovation (i.e. patents) activities.  

(27)   The  betweenness  centrality  (BC)  is  a  measure  of  centrality  of  a  certain  node  (i.e.  v)  in  the  network.  It  is  calculated  as  the  ratio 
between the number of shortest paths between two other nodes (i.e. s and t) and the number of shortest paths between these two 
nodes (s and t) in which v lies. See Righi et al (2020) for more details on the computation of this network statistical indicator.  

31 

                                                        
 
Table 5. Indicators 4: Exploiting the relational dimension of the digital ecosystem 

Indicators 4 

Question 

Indicator 

4.a 

How collaborative is the digital ecosystem?  

How  many  activities  are  the  result  of 
collaborations across players in the  digital 
ecosystem? 

Number  of  collaborative  [non-collaborative]                 
activities (performed by more than one player) 

Share  of  collaborative 
activities (over total number of activities) 

[non-collaborative] 

4.b 

How collaborative are the different type of 
(research or innovation) activities?  

Share  of  collaborative 
activities by type of activity  

[non-collaborative]   

How collaborative are the different type of 
players?  Which  type  of  players  tend  to  be 
more/less collaborative? 

4.c 

How  spatially  concentrated/spread  out  are 
collaborative players? 

Share  of  collaborative 
activities by players’ organizational type 

[non-collaborative]  

Share of collaborative activities performed by 
players 
[different] 
organizational  type  (over  total  number  of 
activities)  

same 

with 

the 

Average  spatial  distance  across  collaborative 
players 
certain 
general/within 
geographical area) 

(in 

a 

How collaborative is a geographical area? 

Share  of  collaborative 
activities per geographical area 

[non-collaborative]  

Within  which  geographical  areas  do 
players  collaborate  more  with  players  in 
the same geographical area (internally)? 

Within  which  geographical  areas  do 
players collaborate more with players in a 
different geographical area (externally)? 

Share  of  internal  collaborative  activities  per 
geographical area  

Share  of  external  collaborative  activities  per 
geographical area  

How  spatially  concentrated  or  spread  out 
are players (independently of them directly 
collaborating) within a certain geographical 
area?  

Average spatial distance across players within 
a certain geographical area 

Average spatial distance across players within 
a certain geographical area 

How  specialized  are  collaborative  [non-
collaborative]  players  in  terms  of  digital 
areas?  

Average number of different digital areas per 
collaborative [non-collaborative] player 

Median  number  of  different  digital  areas  per 
collaborative [non-collaborative] player 

Which  players  or  geographical  area  do 
occupy a strategic position in the network? 

Weighted betweenness centrality (WBC) 

4.d 

4.e 

4.f 

Source: Authors’ elaboration, Digital Economy Unit, JRC. 

4.3  Further applications and extensions of the DGTES  

The presented work describes the first application of the TES methodology to the digital ecosystem (DGTES). 
As first step in the process of mapping the digital ecosystem, a series of indicators originated from the DGTES 
database can be generated, with the aim of providing a comprehensive and policy-relevant overview of the 
global digital landscape.  

However,  the  indicators  and  metrics  presented  above  reflect  just  some  of  the  aspects  and  dimensions  that 
can  be  explored  with  DGTES.  Certainly,  there  is  room  to  extend  the  application  of  the  DGTES  analytical 

32 

 
 
 
 
approach: future works investigating the digital ecosystem using DGTES will build on this report and enlarge 
the scope of the analysis to embrace more dimensions and aspects of the digital ecosystem, and beyond it. 
Some  possible  avenues  of  future  research  can  be  already  pointed  out.  Their  relevance  to  current  EU  policy 
developments makes these avenues of research particularly urgent to explore. 

Using the information on digital areas, DGTES could look more in depth at the technological features of the 
knowledge  generated  and  exchanged  within  the  digital  ecosystem.  The  application  of  Natural  Language 
Processing  (NLP)  methods  like  topic  modelling28  would  enable  the  clustering  of  all  detected  relevant 
documents (associated with relevant activities), allowing to explore the degree of thematic closeness across 
digital areas and to look at potential dynamics of proximity and distancing of technological subdomains over 
time. This would provide novel evidence about the relationship across digital technologies.  

A  deeper  analysis  of  the  collaborations  that  take  place  within  each  digital  area  could  provide  additional 
information on which digital subdomains are more active in producing knowledge, which ones in exchanging 
knowledge through collaborative activities, and whether being active in a certain digital subdomain makes it 
more likely for a player to be active also in another ‘technologically contiguous’ (proxied by closeness) digital 
area.  This  network  of  collaborations  could  be  explored  separately  by  each  digital  area,  allowing  to  draw 
differences  and  similarities  in  the  shape  of  these  networks  in  the  different  digital  subdomains,  and  to  see 
whether and to what extent these networks overlap.  

DGTES  could  also  contribute  to  refining  current  understanding  of  how  knowledge  is  used  in  the  digital 
ecosystem. For instance, using additional information on how many years has a certain patent been renewed 
or  the  analysis  of  patent  citations  could  reveal  the  purpose  of  filing  that  patent  -  whether  it  was  to 
appropriate the return to innovation or, instead, with a more ‘defensive purpose’ to block or discourage other 
players  from  engaging  in  a  certain  digital  technology  or  its  application.  Crossing  these  results  with  the 
information  on  digital  areas  can  give  an  idea  of  which  digital  subdomain  are  emerging  and  are  likely  to 
become critical, as their mastering is expected to ensure technological leadership in the future global digital 
landscape.  

DGTES  could  be  employed  to  investigate  aspects  of  the  digital  ecosystem  related  to  EU  industrial  policies. 
DGTES  could  contribute  to  further  exploring  the  drivers  of  industrial  competitiveness,  by  looking  at  how 
certain  features  of  the  digital  ecosystem  (i.e.  level  of  activity,  location  of  players,  specialization  or 
diversification in different digital areas) can be related to the industrial performance of a given geographical 
area. Given the granularity of the used micro-data, DGTES allows conducting this analysis at different levels, 
both in terms of geographical levels (i.e. country, region, city) and player’s characteristics. For instance, DGTES 
could focus on firms and use the available information on firms’ characteristics (i.e. size, sector) to see how 
these interplay with the digital ecosystem.  

In  this  regard,  DGTES  could  provide  novel  insights  on  the  participation  of  small  and  medium  enterprises 
(SMEs)  in  the  digital  ecosystem.  Accounting  for  over  99%  of  all  European  firms,  small  and  medium 
enterprises (SMEs) are a  vital part of European economy  and society but they have  been slow at taking  up 
digital solutions, often due to lack of available skills, access to finance and markets (COM(2020) 102 final). 
SMEs  are  at  the  core  of  the  Industrial  Strategy,  which  sets  out  measures  and  actions  to  facilitate  their 
engagement  in  the  transition  towards  a  more  digital,  clean,  circular  and  globally  competitive  EU  industry 29. 
Depending  on  the  available  information,  DGTES  could  target  this  specific  type  of  actors  and  provide  an 
original and useful picture of SMEs’ role and positioning within the digital ecosystem.  

DGTES could be used to explore and analyse the intersections between the digital ecosystem and the other 13 
industrial  ecosystems30  identified  by  the  Industrial  Strategy  (COM(2020)  102  final).  Industrial  ecosystems 
tend indeed to be linked to and overlap with each other, as some activities are by nature more horizontal and 
can  be  relevant  for  more  than  one  ecosystem  at  the  same  time.  This  is  clearly  the  case  of  the  digital 
ecosystem: the extreme  permeability of all industrial operations and  processes to digitalization implies that 

(28)   See Righi et al. (2020) for a better discussion of the application of these data processing methods - more specifically, of the Latent 
Dirichlet Allocation (LDA) model - in the case of the techno-economic segment of Artificial Intelligence (AI TES). More information 
available  on  the  AI  Watch  webpage  (https://ai-watch.ec.europa.eu/index_en)  and  on  the  AI  TES  dedicated  dashboard 
(https://web.jrc.ec.europa.eu/dashboard/AI_WATCH_LANDSCAPE/index.html?bookmark=overview). 

(29)   A dedicated strategy for SMEs is adopted in parallel with the New Industrial Strategy (see the Commission’s Communication ‘SME 

strategy for a sustainable and digital Europe’ (COM (2020) 103 final)).  

(30)   The 14 industrial ecosystems are: Aerospace & Defence, Agri-food, Construction, Cultural and Creative Industries, Digital, Electronics, 
Energy Intensive Industries, Energy-Renewables, Health, Mobility-Transport-Automotive, Proximity, Social Economy and Civil Security, 
Retail, Textiles, and Tourism (see COM(2020) 102 final). 

33 

                                                        
 
the  digital ecosystem is intersecting with all other industrial ecosystems, transforming the way in  which all 
other  ecosystems  perform  their  core  activities,  and  defining  the  technological  scope  in  which  they  operate. 
Thus, analysing and better understanding the digital ecosystem is key to leverage a successful twin transition 
in the whole economy. A cross-ecosystem analysis of the industrial landscape at global as well as at EU level 
could be one of the main and most policy-relevant contributions of the DGTES.  

Finally, taking a step back from the specific focus on the digital ecosystem and looking at the whole economy, 
the  TES  methodological  approach  could  be  applied  to  any  other  industrial  ecosystem,  obtaining  valuable 
knowledge  about  who  does  what,  with  whom,  where  and  when.  Focusing  on  the  needs  of  players  within 
diverse  industrial  ecosystems’  value  chains,  the  New  Industrial  Strategy  seems  to  optimally  match  the 
possibilities  offered  by  the  TES  analytical  approach.  The  aggregated  micro-data  approach  of  TES,  based  on 
multiple integrated factual data sources, can be used to identify the boundaries of each industrial ecosystem 
and to provide insights about players, technologies, and  processes within each ecosystem.  A comprehensive 
picture of this ‘system of ecosystems’  can contribute to  advising  decision-makers with the design of better 
informed and more forward-looking policies aimed at filling actual and foreseeable gaps as well as at further 
strengthening areas of excellence of the European economy. 

34 

 
 
 
 
5  Conclusions: DGTES for a digital, sustainable, resilient and globally 

competitive EU economy  

This  section  discusses  and  interprets  how  the  application  of  the  Techno-Economic  ecosystem  (TES) 
methodological  approach  to  the  analysis  of  the  digital  ecosystem  (DGTES)  can  contribute  to  informing, 
assessing and ultimately improving the policies aiming at transforming the European Union (EU) into a more 
sustainable,  digital,  resilient  and  globally  competitive  economy.  The  first  subsection  briefly  summarizes  the 
main challenges and opportunities EU’s digital and industrial policies face in the light of uncertain and fast-
changing  global  circumstances.  The  second  subsection  reviews  how  DGTES  can  contribute  to  addressing 
current policy-relevant issues. 

5.1  Fostering digital transformation in an changing global landscape: a renewed 

policy challenge for the EU 

Digital technologies  have  been changing the face of societies, economies and industries for a few decades. 
During  past  years,  the  extent  to  which  digital  technologies  permeate  all  aspects  of  economic  and  social 
activities has dramatically expanded, as these technologies have become an imperative for working and doing 
business, communication and  social interactions, learning and entertaining, shopping  and accessing a  broad 
range  of  services  from  health  to  culture.  Digital  technologies  are  nowadays  generating  an  ever-increasing 
amount  of  data,  which  can  lead  to  a  completely  new  means  and  levels  of  value  creation,  establish  new 
business models, allow industry to be more productive and provide workers with new skills.  

As  powerful  drivers  of  the  green  transition,  digital  solutions  can  also  significantly  contribute  to  the 
achievement  of  the  European  Green  Deal  goals  by  supporting  circular  economy  with  a  fully  integrated  life-
cycle  approach  -  from  design  through  sourcing  of  energy,  raw  materials  and  other  inputs  to  final  products 
until  the  end-of-life  stage  -  ,  helping  achieve  carbon-neutrality  in  all  sectors  and  reduce  environmental 
footprints.  This  remarks  how  the  digital  transformation  should  go  hand-in-hand  with  the  green  transition, 
pursuing  the  aim  of  not  increasing  any  further  the  environmental  burden  of  digital  actives  (i.e.  in  terms  of 
total electricity use and emissions) but instead  significantly contributing to the transition towards a climate 
neutral, circular, and resilient European economy and society. 

The COVID-19 pandemic has reinforced the importance of digital transformation for the resilience of societies 
and  economies,  and  accelerated  its  pace.  At  the  same  time,  the  pandemic  crisis  has  exposed  the 
vulnerabilities of the European digital space and its dependencies on non-European technologies and players. 
Driven  by  concerns  about  economic  prosperity,  competitiveness,  and  supply  chains  resilience  that  emerged 
during the COVID-19 crisis, EU policies such are the Digital Decade and the Industrial Strategy have remarked 
the importance of reinforcing Europe’s industrial and strategic autonomy to enhance industrial capacity and 
digital  transformation.  Coupled  with  a  period  of  uncertainty  and  fast  changing  global  circumstances,  this  is 
posing new challenges for Europe as it sets off on the twin transition.  

To be technologically and digitally sovereign in an interconnected world, Europe needs to assess and address 
strategic  weaknesses,  vulnerabilities  and  high-risk  dependencies  in  the  most  critical  and  emerging  digital 
technologies,  which  endanger  the  achievement  of  its  digital  and  industrial  ambitions.  At  the  same  time, 
strategic dependencies could also represent a window of opportunity for Europe’s industry to develop its own 
products and services in critical areas, contributing to boosting competitiveness and industrial leadership. In 
this  case,  efforts  and  resources  could  be  invested  to  retain  existing  strengths  and  gain  position  in  digital 
domains where no global incumbents have yet emerged. Yet, all this requires Europe to develop and further 
strengthen  its  own  technological  capabilities  and  capacities  in  a  way  that  enables  people,  societies  and 
businesses to fully grasp the benefits of the digital transformation.  

In  these  challenging  times,  Europe’s  aspiration  to  pursue  digital  transformation  to  seize  a  human  centred, 
sustainable and more prosperous digital future is more relevant than ever. This calls for urgent, ambitious and 
coherent policy initiatives. As discussed in the Introduction, several existing EU policies and programmes are 
consistent in their visions and goals of digital sovereignty and leadership, and in line with the objectives of the 
European  Green  Deal.  However,  to  avoid  the  risk  of  these  remaining  a  too  high-level  agenda  without  clear 
references for the implementation of concrete plans, these policies need to be complemented and competed 
by  sound science-based analyses to identify  European strengths and weaknesses  compared to other  global 
leaders, as well as to monitor strategic control points and bottlenecks in critical technological domains. Thus, 
policies  aiming  at  fostering  the  digital  transition  and  reducing  Europe’s  digital  dependency  need  to  be 
grounded  on  a  better  and  more  comprehensive  understanding  of  the  features  and  structure  of  the  digital 

35 

ecosystem,  in  isolation  as  well  as  in  its  intersections  with  the  other  industrial  ecosystems.  In  this  respect, 
DGTES represents a valid instrument to conduct the needed policy-oriented analyses.  

5.2  A policy-oriented analytical instrument to steer digital transformation  

DGTES is by design a policy relevant analytical instrument. As explained in the previous sections of this report, 
DGTES can provide quantitative indicators and metrics adequate to map the spatial distribution, technological 
development,  and  evolution  of  a  complex  and  dynamic  industrial  ecosystem.  Based  on  a  rigorous  scientific 
methodology  to  integrate  multiple  micro-data  bases,  the  DGTES  analytical  approach  allows  to  look  with  a 
minimum time delay at highly dynamic, integrated technological domains such as the digital ecosystem. This 
almost real-time possibility allows DGTES to play a key role in supporting the design and implementation of 
timely policy initiatives fostering digital transformation. Mostly important, the TES methodological approach at 
the ground of DGTES can potentially be applied to any techno-economic ecosystem. 

The policy space related to the digital ecosystem lies at the intersection of major policy programmes for EU’s 
resilience  and  competitiveness,  such  as  the  New  Industrial  Strategy,  the  Digital  Decade,  and  the  New 
Innovation  Agenda.  The  application  of  DGTES  can  thus  offer  policy-relevant  recommendations  for  achieving 
the goals of digital sovereignty and leadership pursued by these policies. DGTES can help deal with a series of 
issues that, if not timely addressed, could jeopardize the effective implementation of policy initiatives for a 
more digital, sustainable, resilient and globally competitive EU economy.  

—  Monitoring EU’s role in shaping the evolution of emerging digital technologies 

DGTES  can  provide insights  on the  patterns of evolution  of digital technologies. It  can identify which digital 
subdomains have expanded  more over time and are expected to reveal particularly dynamic trajectories.  At 
the same time, by relating this information with the geographical composition of the digital ecosystem, it can 
shed lights on which global players are driving and steering the observed technological dynamism. This allows 
monitoring EU’s participation in shaping the evolution of the current and future global digital landscape. One 
example is the use of information derived from the analysis of patent data to generate DGTES indicators such 
as  the  specialisation  in  specific  digital  areas,  which  allows  detecting  possible  gaps  in  strategic  emerging 
digital subdomains and eventually intervening with the design of appropriate policies. 

—  Assessing features and properties of collaborative networks in the global digital ecosystem  

By identifying global players in different digital subdomains, DGTES can describe and assess the distribution 
of  competences  and  capabilities  within  the  digital  ecosystem,  even  at  different  levels  of  aggregation  (i.e. 
regional,  national  or  local).  The  micro-data  foundation  of  the  DGTES  methodology  allows  drawing  the 
networks  of  interlinkages  and  connections,  which  are  at  the  same  time  the  outcomes  and  drivers  of  these 
clusters  of  competences  and  capabilities.  Furthermore,  the  analysis  of  explicit  and  implicit  (or  ‘latent’) 
communities spanning over multiple vertical topics allows to explore and make hypotheses on possible effects 
(in terms of strengths as well as possible weaknesses) on the whole system. This information is important for 
shaping targeted policy interventions aiming at strengthening the connection between research and market-
oriented activities, in order to increase the leverage technological innovation as driver of competitiveness and 
productivity.  

—  Complementing  the  Industrial  Strategy  by  highlighting  the  overlaps  and  interactions  of  the  digital 

ecosystem with other industrial ecosystems 

DGTES allows taking into account the transversal and horizontal nature of the digital ecosystem and exploring 
its intersections with other industrial ecosystems. The results of these cross-ecosystem analysis contribute to 
understanding the interactions within as well as across ecosystems, which is important for better informing 
policy  initiatives  supporting  a  stronger  interaction  and  exchange  across  industrial  ecosystems.  For  example, 
DGTES  can  reveal  the  presence  and  the  spatial  distribution  of  multi-ecosystem  clusters  of  competences 
capabilities that could be targeted to leverage an effective allocation of policy initiatives fostering industrial 
competitiveness.  

—  Supporting the Innovation Agenda with original indicators and metrics 

DGTES can support the implementation and assessment of the initiatives related to the Innovation Agenda to 
improve EU’s innovation performance and foster the wave of deep tech innovation with original indicators and 
metrics.  Besides  offering  a  timely  snapshot  of  EU’s  position  in  terms  of  leadership  in  scientific  knowledge 
production  and  exchange  with  respect  to  other  global  competitors,  DGTES  allows  relating  EU’s  scientific 
capabilities in the realm of digital technologies with the Research and Innovation (R&I) activities conducted at 

36 

firm  level  and  with  the  performance  of  the  industrial  base.  The  possibility  to  investigate  and  explore  the 
features of partnerships and collaborations between companies and research actors through EU programmes 
can  contribute  to  a  better  understanding  of  their  role  in  driving  innovation  across  Europe.  The  geographical 
nuances of DGTES can also provide novel insights about how to measure the innovation divide across Member 
States (MSs) and European regions and to address internal cohesion.  

—  Detecting digital areas that are critical for EU’s open strategic autonomy  

DGTES  allows  for  the  exploitation  of  quantitative  and  qualitative  indicators  to  gain  knowledge  through 
technology  intelligence.  Unlike  standard  statistical  methodologies,  DGTES  can  reveal  hidden  trends  in  data, 
anticipating strategic dependencies in key and  critical digital technologies, and revealing  patterns that  draw 
attention to emerging vulnerabilities and potential bottlenecks. This information can provide relevant insights 
to design and assess policy initiatives aiming at strengthening EU digital leadership and sovereignty, such as 
the New Industrial Strategy and the European Chips Act 31. Such an analysis would also allow to determine the 
extent to which individual MSs can rely on their own or intra-European resources as well as, on the other side, 
they remain dependant from other, extra-EU players. 

The results of DGTES can contribute to the formulation of concrete policy instruments, such as a toolbox of 
interventions to mitigate EU strategic dependencies in key industries and critical digital subdomains. Table 6 
suggests  some  examples  of  ‘how  DGTES  could  contribute’  to  informing,  steering  and  improving  EU  policies 
related to digital transformation, technological sovereignty, and industrial leadership.  

Figure 3 concludes by summarizing how the results of the analyses conducted using the DGTES approach can 
provide novel evidence-based insights to the debate on current EU policy priorities.   

Table 6. How DGTES can contribute to policy-relevant issues 

Policy-relevant issues  

DGTES potential contributions  

Industrial competitiveness 

What  is  the  relationship  between  research, 
innovation  and  industrial  performance  in  the 
digital ecosystem?  

Reveal the interlace of industrial, innovation and research 
activities  

Describe 
industrial players involved in the digital ecosystem 

industrial  composition  and  capabilities  of 

How  do 
research  and 
translate into industrial performance? 

innovation  outputs 

In this respect, what is the situation of SMEs in 
the digital ecosystem? 

Relate 
their 
industrial  player’s  performance  with 
characteristics and their level of involvement in the digital 
ecosystem 

Zooms  on  different  dimensions  and  levels,  with  focus  on 
SMEs 

How  do  digital  technologies  influence  other 
industrial ecosystems?  

Identify  activities  and  players  that  lay  at  the  intersection 
of different industrial ecosystems  

Emerging digital technologies 

Which  are  the  technological  boundaries  of  the 
digital ecosystem?  

Which  are 
technological 
ecosystem?  

the  most 
subdomains 

critical  emerging 
the  digital 

in 

Describe  the  technological  composition  of  the  digital 
ecosystem  by  mapping  the  main  digital  subdomains 
identified by the digital areas (worldwide, EU, regional) 

Explore  proximity  and  possible  synergies  across  different 
digital subdomains (digital areas) 

How  is  the  technological  frontier  in  digital 
technologies moving? Are there weak signals of 

Identify  weak  signal  of  emerging  digital  subdomains  
digital  (digital  areas)  that  are  pushing  forward  the  digital 

(31)  See the Commission’s communication ‘A Chips Act for Europe’ (COM(2022) 45 final).  

37 

 
                                                        
 
emerging technological trajectories? 

frontier  

Digital leadership 

the 

are 

(research, 
Where 
technological,  production/industrial)  capabilities 
localized?  

clusters 

of 

How  are 
characterized?  

these 

clusters  of 

capabilities 

How  is  knowledge  generated  and  shared  in  the 
digital 
across 
geographical areas?  

ecosystem,  within 

and 

Which  are  the  main  players  in  the  digital 
ecosystem  in  terms  of  knowledge  generation 
and exchange? What is the position of the EU?   

Identify  the  stakeholders,  players,  and  critical  mass  of 
capabilities by geographical area (worldwide, EU, regional) 

Describe and explore their features/properties 

Analyse knowledge generation and exchange (as activities 
of  research  and  innovation)  across  players  and  within 
(implicit  and  explicit)  communities  in  the  EU    (worldwide, 
EU, regional) 

Describe  and 
features/properties  of 
the 
collaborative  networks  within  the  EU  (in  activities  of 
research and innovation) 

explore 

Digital sovereignty and strategic autonomy 

How  does  the  EU  participate  in  shaping  and 
steering the evolution of digital technologies?  

Identify  the  players  shaping  the  evolution  of  digital 
subdomains (digital areas) in the digital ecosystem 

Which  actors  have  a  strategic  position  and 
control  the  network  in  the  digital  ecosystem? 
What is the position of the EU?  

In  which  digital  areas  is  the  EU  stronger,  more 
competitive? And in which digital areas is the EU 
weaker, 
less  competitive?  Where  does  the 
ecosystem present bottlenecks or vulnerabilities 
that  could  turn  into  strategic  dependencies  for 
the  EU?  How  can  we  identify  and  monitor  the 
technology  gap  at 
root  of  strategic 
the 
dependencies? 
In  which  has  the  EU  more 
potential? (window of opportunity) 

Source: Authors’ elaboration, Digital Economy Unit, JRC. 

Identify  gatekeepers  and  central  nodes  in  the  ecosystem, 
to  assess  the  presence  of  bottlenecks  and  potential 
vulnerabilities  in  terms  of  players  involved  (number  and 
characteristics), 
technological 
subdomains (digital areas) 

knowledge, 

types  of 

Measure  the  technology  gap  of  the  EU  with  respect  to 
other global players 

38 

 
Figure 3. DGTES for EU policy priorities 

Source: Authors’ elaboration, Digital Economy Unit, JRC. 

39 

  
 
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41 

 
 
List of abbreviations and definitions  

AI 

ATI 

BC 

EC 

EU 

Artificial Intelligence 

Advanced Technologies for Industry 

Betweenness centrality 

European Commission 

European Union 

DESI 

Digital Economy and Society Index 

DGTES 

Digital Techno-Economic ecoSystem 

GDP 

JRC 

IaaS 

Gross Domestic Product 

Joint Research Centre 

Infrastructure as a service  

IDITES  

Impact of Digital Transformation on the Economy and Society (project) 

LDA 

MS 

NLP 

Latent Dirichlet Allocation  

Member State 

Natural Language Processing  

PaaS 

Platform as a service 

PPS 

Purchasing Power Standards  

PREDICT  Prospective Insights on ICT R&D (project) 

RCA 

R&D 

R&I 

Revealed comparative advantage 

Research & Development 

Research & Innovation  

SaaS 

Software as a service 

SMEs 

Small and medium enterprises 

TES 

Techno-Economic ecoSystem  

WBC 

Weighted betweenness centrality  

42 

List of boxes  

Box 1. TES: mapping the landscape of dynamic techno-economic ecosystems from a policy-relevant 
perspective .......................................................................................................................................................................................................................... 14 

Box 2. Identifying the digital areas .................................................................................................................................................................... 19 

Box 3. Generating the final DGTES database: from keywords to queries.................................................................................. 21 

Box 4. Generating the final DGTES database: from documents to players .............................................................................. 23 

43 

 
List of figures  

Figure 1. The TES analytical approach: specific features .................................................................................................................... 14 

Figure 2. DGTES workflow and methodology .............................................................................................................................................. 25 

Figure 3. DGTES for EU policy priorities .......................................................................................................................................................... 39 

44 

 
List of tables  

Table 1. Digital areas in DGTES ............................................................................................................................................................................ 19 

Table 2. Indicators 1: Players and activities in the digital ecosystem ......................................................................................... 27 

Table 3. Indicators 2: Adding the geographical dimension to the digital ecosystem ......................................................... 28 

Table 4. Indicators 3: Adding the technological dimension to the digital ecosystem ........................................................ 30 

Table 5. Indicators 4: Exploiting the relational dimension of the digital ecosystem .......................................................... 32 

Table 6. How DGTES can contribute to policy-relevant issues ......................................................................................................... 37 

45 

 
 
Annexes  

Annex 1. Identification of digital areas 

List of documents consulted for the initial identification of digital areas, by repository. 

From JRC repository 32 

Digital 
(https://publications.jrc.ec.europa.eu/repository/handle/JRC116179) 

Transformation 

in 

Transport,  Construction,  Energy,  Government  and  Public  Administration 

Global 
(https://publications.jrc.ec.europa.eu/repository/handle/JRC124742) 

Innovation 

Chains 

Networks 

Value 

and 

in 

the 

Fourth 

Industrial 

Era 

Mobility 
(https://publications.jrc.ec.europa.eu/repository/handle/JRC125412) 

Imaginaries: 

Social  & 

Ethical 

Issues 

The 

of 

Connected 

and 

Automated 

Vehicles 

From DG GROW repository 33 

New  technologies  and  digitisation:  Opportunities  and  challenges  for  the  social  economy  and  social  enterprises 
(EASME/COSME/2017/024), 
(https://c.europa.eu/growth/content/new-technologies-and-digitisation-
opportunities-and-challenges-social-economy-and-social_en)  

06.10.2020 

Definition of the digital building logbook – Report 1 of the study on the development of a European Union framework 
for  buildings'  digital  logbook,  02.10.2020  (https://op.europa.eu/en/publication-detail/-/publication/cacf9ee6-06ba-
11eb-a511-01aa75ed71a1/language-en) 

Supporting 
(https://ec.europa.eu/docsroom/documents/38281) 

digitalisation 

the 

of 

construction 

sector 

and 

SMEs. 

31.10.2019 

From DG CNECT – Horizon Europe Research and Innovation 34 repository  

Shaping the digital (r)evolution in agriculture – EIP Agri (https://ec.europa.eu/eip/agriculture/sites/default/files/eip-
agri_brochure_digital_revolution_2018_en_web.pdf) 

EU auditors, not just lawmakers, recognise the potential of digital farming tools 
(https://www.euractiv.com/section/agriculture-food/news/eu-auditors-not-just-lawmakers-recognize-the-potential-of-
digital-farming-tools/) 

Digital Farming: Driving productivity and a more sustainable way of farming 
(https://www.euractiv.com/section/agriculture-food/video/digital-farming-driving-productivity-and-a-more-
sustainable-way-of-farming/) 

Internet of farms to reduce red tape and enable more transparency (https://www.euractiv.com/section/agriculture-
food/news/internet-of-farms-to-reduce-red-tape-and-enable-more-transparency/) 

Digitising European Industry (http://ec.europa.eu/newsroom/horizon2020/document.cfm?doc_id=40217) 

The role of digital technologies in the EU agricultural research & innovation strategy 
(http://ec.europa.eu/newsroom/horizon2020/document.cfm?doc_id=40218) 

DIGITIZING THE AGRI-FOOD SECTOR (http://ec.europa.eu/newsroom/horizon2020/document.cfm?doc_id=40220) 

Research workshop on Digitising the Agri-food Sector: a research agenda for Horizon 2020 
(http://ec.europa.eu/newsroom/horizon2020/document.cfm?doc_id=40233) 

Flightpath 2050 Europe’s Vision for Aviation 
(https://ec.europa.eu/transport/sites/default/files/modes/air/doc/flightpath2050.pdf) 

EU project WATCH(es)-OVER people in transit (https://cordis.europa.eu/article/id/30895-eu-project-watchesover-
people-in-transit) 

Vision Inspired Driver Assistance Systems (https://cordis.europa.eu/project/id/690772) 

COSAFE (Cooperative Connected Intelligent Vehicles for Safe and Efficient Road Transport) 
(https://cordis.europa.eu/project/id/824019/reporting) 

32   https://publications.jrc.ec.europa.eu/repository/handle/JRC126053 
33     https://ec.europa.eu/growth/publications_en 
34   https://ec.europa.eu/info/research-and-innovation/research-area_en 

46 

                                                        
 
Digital technology improves safety at sea (https://ec.europa.eu/research-and-innovation/en/projects/success-
stories/all/digital-technology-improves-safety-sea) 

ZERO-EMISSION WATERBORNE TRANSPORT 
(https://www.waterborne.eu/images/documents/201021_SRIA_Zero_Emission_Waterborne_Transport_spread.pdf) 

Rail 2030 – Research & Innovation Priorities (https://errac.org/wp-
content/uploads/2019/09/errac_rail_2030_research_and_innovation_priorities.pdf) 

Big data offers big gains for transport operators (https://ec.europa.eu/research-and-innovation/en/projects/success-
stories/all/big-data-offers-big-gains-transport-operators)  

European Project “SCORE” (https://cordis.europa.eu/project/id/784960/it) 

European Project “FLORA ROBOTICA” (https://cordis.europa.eu/project/id/640959) 

European Project “SubCULTron” (https://cordis.europa.eu/project/id/640967) 

European project “COMPARE” (https://cordis.europa.eu/project/id/643476) 

Knowledge Centre for Bioeconomy (https://knowledge4policy.ec.europa.eu/bioeconomy_en) 

Deploying the bioeconomy in the EU (https://op.europa.eu/en/publication-detail/-/publication/2cf89630-e2bc-11eb-
895a-01aa75ed71a1/) 

European Project "BIGCLOUT" (https://cordis.europa.eu/project/id/723139) 

European Proje ct "METAFLUIDICS" (https://cordis.europa.eu/project/id/685474) 

European Project "COSY-BIO" (https://cordis.europa.eu/project/id/766840) 

European Project "SYMBIOTIC" (https://cordis.europa.eu/project/id/637107) 

From other repositories of the European Commission  

Digital transformation scoreboard (2018) - EU businesses go digital: Opportunities, outcomes and uptake 
(https://op.europa.eu/en/publication-detail/-/publication/683fe365-408b-11e9-8d04-01aa75ed71a1) 

The Digital Economy and Society Index (DESI) (https://ec.europa.eu/newsroom/dae/document.cfm?doc_id=67086) 

2030 Digital Compass: the European way for the Digital Decade (https://eur-lex.europa.eu/legal-
content/en/TXT/?uri=CELEX%3A52021DC0118) 

Digital Europe Programme (https://digital-strategy.ec.europa.eu/en/activities/digital-programme)  

From repositories of other organizations  

OECD Report: a roadmap toward a common framework for measuring the digital economy 
(https://www.oecd.org/sti/roadmap-toward-a-common-framework-for-measuring-the-digital-economy.pdf) 

ETSI Technology Radar (https://www.standict.eu/sites/default/files/2021-05/etsi_wp45_ETSI_technology_radar.pdf) 

CEN/CENELEC Work Programme 2021 (https://www.cencenelec.eu/news-and-events/news/2021/publications/2021-
01-25-cen-and-cenelec-work-programme-2021/?Sectors[]=6945) 

47 

 
 
 
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