RESEARCH ARTICLE - IA de Investigación especializada en el MIT

ARTÍCULO DE INVESTIGACIÓN

Who influences policy labs in the European Union?
A social network approach

Esteban Romero-Frías1,2

, Daniel Torres-Salinas1,3,4

, and Wenceslao Arroyo-Machado1,3

1Medialab UGR, University of Granada, Granada, España
2Department of Accountancy and Finance, University of Granada, Granada, España
3Department of Information and Communication Sciences, University of Granada, Granada, España
4EC3metrics Spin-off, University of Granada, Granada, España

Palabras clave: European Union, influencers, policy labs, public innovation, social networking analysis,
Twitter

ABSTRACTO

The growing importance of public innovation has been manifested through the creation of
policy labs: spaces for policy experimentation and innovation that work for or within a
government entity. The rise of this phenomenon in Europe was evidenced by the creation of
a policy lab by the European Commission (EC) en 2016 and the publication by the EC of a
report identifying policy labs and their influencers in Europe. Public innovation is increasingly
based on national and international networks, giving rise to complex ecosystems involving
participation by multiple actors from countries with different administrative approaches. Nuestro
study uses social network analysis of these labs’ Twitter profile data to map the European
Union’s (EU) public innovation ecosystem and identify the major influencers. Policy labs and
their influencers are analyzed by administration style by using a large geographical database.
The results reveal a complex global network of influencers and a strong predominance of
the Anglo-Saxon administration style. From an EU perspective, our systematic analysis of
influence is particularly important in the post-Brexit context, helping to foster a genuine public
innovation ecosystem that is both autonomous and interconnected with the aim of facing
challenges such as the Sustainable Development Agenda and COVID-19 crisis recovery.

INTRODUCCIÓN

Public sector innovation has become one of the most promising topics in public governance in
recent decades (moore, 2005) and this has led to its being institutionalized (Hjelmar, 2019). El
speed with which social and technological changes occur, the complexity of social problems
and a globalized world—thanks to trade, social networks, and information flows—have pushed
governments to adopt various solutions to promote public innovation (OECD, 2019) y, de este modo,
address common challenges more creatively, more effectively, and with greater social legiti-
macy. As Hartley (2005) pointed out, public innovation is a process of change that produces
durable and significant transformation in how an organization operates. This happens not just
by creating original ideas but also as the result of applying existing ideas to new institutional
contextos (Gieske, George et al., 2020; moore, 2005). Efforts to advance public innovation have
been made at various levels of government administration, from local to supranational levels.

Innovation labs, innovation networks, innovation schemes, and prizes are some examples of
the institutionalization of public innovation (OECD, 2017). In the present article, we focus on
innovation labs or policy labs in Europe, whose mission it is to use innovative and participatory

un acceso abierto

diario

Citación: Romero-Frías, MI., Torres-
Salinas, D., & Arroyo-Machado, W..
(2023). Who influences policy labs in
European Union? A social network
acercarse. Quantitative Science
Estudios, 4(2), 423–441. https://doi.org
/10.1162/qss_a_00247

DOI:
https://doi.org/10.1162/qss_a_00247

Revisión por pares:
https://www.webofscience.com/api
/gateway/wos/peer-review/10.1162
/qss_a_00247

Recibió: 10 December 2021
Aceptado: 28 Enero 2023

Autor correspondiente:
Wenceslao Arroyo-Machado
wences@ugr.es

Editor de manejo:
Juego Waltman

Derechos de autor: © 2023 Esteban Romero-
Frías, Daniel Torres-Salinas, y
Wenceslao Arroyo-Machado. Publicado
bajo una atribución Creative Commons
4.0 Internacional (CC POR 4.0) licencia.

La prensa del MIT

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
q
s
s
/
a
r
t
i
C
mi
–
pag
d

F
/

4
2
4
2
3
2
1
3
6
4
3
3
q
s
s
_
a
_
0
0
2
4
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Who influences policy labs in the European Union?

methodologies in designing public policies (Hjelmar, 2019; Luis, McGann & Blomkamp, 2020;
Romero-Frías & Arroyo-Machado, 2018; Tõnurist, Kattel, & Lember, 2017). Labs are attempts to
institutionalize and promote the transformation of public organizations from the inside out, en
order to respond to citizens’ demands for openness and participation. Al mismo tiempo, they seek
to activate an approach in which smart citizens and employees foster collective intelligence.
Policy labs place citizens at the center of their innovation processes in open, experimental ways
to promote more proactive institutions that seek to recover the political initiative and increase
people’s confidence in them. Concepts such as coproduction, citizen participation in innovation
procesos, experimentation, and managing through networks all fit within the New Public
Governance paradigm (Pollitt & Bouckaert, 2011; Torfing, Andersen et al., 2020). This new
theoretical approach takes into account sociology and network theory and acknowledges
the increasing uncertainty affecting public management in the 21st century (Osborne,
2010), a political scenario that policy labs are seeking to address in the public sector.

Policy labs are generally identified by a range of names: government labs or govlabs, público
sector innovation (PSI) labs (McGann, Blomkamp, & Luis, 2018), innovation labs (Tõnurist
et al., 2017), or policy labs (Batán & Lochard, 2016), among others. Throughout this study, nosotros
will use the term policy lab (or simply, labs), as this was used by the European Commission
(EC) when creating its own lab in 2016.

To understand the global outreach of policy labs in Europe and around the world, es
necessary to adopt various means of classifying public administration according to their
características. As stated, policy labs meet governments’ needs to reorganize their innovation
procesos, taking into account two main ideas: technological change and user- and citizen-
centric governance and management. Based on an analysis of the literature, Voorberg,
Bekkers, and Tummers (2011) identified the following drivers of the innovation environment:
the social and political complexity of the public organization environment; the characteristics
and degree of the legal culture in a country; the type of governance and state tradition in the
country; and the allocation of resources, resource dependency, and the quality of relationships
within the networks among the stakeholders involved. Both the legal culture and the type of
governance and state tradition seem to be relevant drivers to understand the nature of policy
labs. Por ejemplo, innovation in the public sector is dependent on the capacity to embark on a
process of “trial and error,” experimentation, colaboración, and availability of resources.
Dwivedi (2005, pag. 20) refers to administrative culture as “the modal pattern of values, creencias,
attitudes, and predispositions that characterize and identify any given administrative system.”
This idea, also referred to as Administration Style, is used in this study to classify labs according
to their different national traditions. Administration Style has been used to explain the evolu-
tion of public sector reforms (Bonsón & Bednárová, 2018), including aspects such as transpar-
ency, accountability, and e-participation (Pina, Torres, & Royo, 2007; Royo, Yetano, &
Acerete, 2014). According to these authors, the dissemination of public sector management
innovations is influenced by their organizational and administrative culture, historical back-
ground, and legal structure. Por lo tanto, this can be a useful theoretical approach to understand
the creation and popularization of policy labs and their influencers across countries.

In EU countries, four broad styles of public management may be distinguished, como sigue

(Torres, 2004):

(cid:129) Anglo-Saxon (AS, including Ireland and the United Kingdom): emphasis on efficiency, effec-
tiveness, and value for money in public administration; more likely to introduce market
mechanisms, notions of competitiveness, and attempts to make public services more respon-
sive to their users or customers; adaption of private sector experience to the public sector.

Estudios de ciencias cuantitativas

424

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
q
s
s
/
a
r
t
i
C
mi
–
pag
d

F
/

4
2
4
2
3
2
1
3
6
4
3
3
q
s
s
_
a
_
0
0
2
4
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Who influences policy labs in the European Union?

(cid:129) Germanic (GE, including Austria and Germany): Weberian approach, involving a com-
plex federal system; administrative practice marked by an overriding legalistic philoso-
phy within the constitutional framework; strong hierarchy with detailed regulations.
(cid:129) Nordic (NO, including Denmark, Los países bajos, Finland, Suecia, and Norway): a
mixture of the AS and Germanic types; public administration model that seeks efficiency
and effectiveness through the satisfaction of citizens’ needs; strong tradition of negotia-
tion and consultation.

(cid:129) Southern EU countries (SO, including Belgium, Francia, Greece, Italia, Luxembourg,
Portugal, and Spain): influenced by the French legal model focused on administrative
law; definition by central government of overarching state rules for field services and a
unitary treasury system.

This classification focuses mainly on Western Europe, which may a priori present limita-
tions due to the enlargement of the EU with countries that have postsocialist administration
traditions (Painter & Peters, 2010). Sin embargo, the EU report upon which this research is based
does not identify policy labs from these countries and therefore its scope fits the aforemen-
tioned classification (es decir., mainly focused on Western European tradition).

Public innovation practice in Europe and around the world is made up of globalizing instru-
mentos, such as conferences and networks, giving rise to a complex and interconnected public
innovation ecosystem. Policy labs are one of the most novel and relevant entities in this eco-
sistema. Due to their institutional nature and goals, most of them have active profiles on social
media, where they communicate and build networks with other actors.

Our main objective is to explore this global ecosystem by analyzing structural relations on
the Twitter profiles of European labs to obtain a better understanding of public innovation
communities and the extent to which they are connected to the different public administration
styles. This can provide insights on how different management and innovation cultures relate
to each other in a field that, unlike competitive political processes such as electoral cam-
paigns, is built on cooperative dynamics (Sørensen & Torfing, 2011). Además, Dawes
and Gharawi (2018) pointed out the lack of empirical research into Transnational Public Sec-
tor Knowledge Networks, in which governments engage to share knowledge and information.
This study helps to fill this research gap in relation to policy labs, which are an example of this
type of network, as well as verifying the effectiveness of new media as a means of unveiling
new actors and potential partners.

Based on this general goal, our research questions are the following:

RQ1: What kind of network exists between European policy labs and their potential influ-

encers (their followed profiles or friends)?

RQ2: What is the profile of European policy lab influencers on Twitter? Does social network
análisis (SNA) validate the identification by experts of influencers in the EC-
commissioned report?

RQ3: How are policy lab influencers distributed geographically and according to their

public administration style?

2. POLICY LABS IN EUROPE
En 2016 the EC founded its own policy lab1, defined as a “a space designed to foster creativity
and engagement, and to develop interactions, processes and tools able to bring innovation

1 https://blogs.ec.europa.eu/eupolicylab/.

Estudios de ciencias cuantitativas

425

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
q
s
s
/
a
r
t
i
C
mi
–
pag
d

F
/

4
2
4
2
3
2
1
3
6
4
3
3
q
s
s
_
a
_
0
0
2
4
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Who influences policy labs in the European Union?

into European policy-making”2. It was conceived of as both a physical and conceptual space
where solutions are delivered through testing, experimenting, codesigning and visual thinking,
using participatory methods that integrate all the stakeholders’ views. In June 2016, the EC
Joint Research Centre, on which the lab depends, commissioned and published a report enti-
tled “Public Policy Labs in European Union Member States.” This was prepared by Conseil &
Recherche and the 27e Région (Batán & Lochard, 2016) and their main goal was to map the
policy labs operating in the EU at that time—64 laboratories in 13 countries—and their
principal topics of interest. A lo mejor de nuestro conocimiento, this report has not yet been replaced
or updated.

Hasta ahora, research into policy labs in Europe has been scant, although recently there has been
a growing number of studies on policy design and labs (Olejniczak, Borkowska-Waszak et al.,
2020; van Buuren, Lewis et al., 2020). To our knowledge, some studies have analyzed Euro-
pean labs in a wider context. Por ejemplo, Olejniczak, Newcomer, and Borkowska-Waszak
(2016) analyzed 20 labs around the world, including Europe, through a literature review and
analysis of policy evaluation practices, to determine their value and impact. Tõnurist et al.
(2017) analyzed 35 public innovation laboratories around the world to determine their main
characteristics and the reasons for their creation. Their methodology focused on empirical
analysis by triangulating data from in-depth interviews, document analysis and a survey of
i-labs. McGann et al. (2018) tried to understand the role of public sector innovation labs in
policy systems by analyzing publicly available information of a sample of 20 labs worldwide.
Focusing solely on the European context, Romero-Frías and Arroyo-Machado (2018) used net-
work analysis to reveal the structure of relationships between the 42 del 64 labs in the EC
report that have a presence on Twitter, conducting a content analysis of the labs’ Twitter pub-
lications to identify their topics of interest. More recently, Olejniczak et al. (2020) analyzed the
funciones, estructuras, and processes of 20 policy labs from all around the world, incluido
Europa.

The EC report mapped the main European policy labs and offered a selection made by the
report’s authors of the so-called “influencers” of these policy labs. Mesa 1 shows a list of 13
influencers that are defined as “entities that both advocate and propel the creation of Policy
Labs, but are not in and of themselves attached to a specific government organization.” Influ-
encers play a role in advocating the creation of policy labs and providing experience,
resources, and networking, as well as functioning as central nodes in the policy lab manager
network.

3. SOCIAL NETWORK ANALYSIS AND INFLUENCER IDENTIFICATION

Social network analysis is based on network theory, which enables us to understand and
model complex systems (Luis, 2008). Different types of graph reflect real-world behavior
through individual participants (nodos), and the implicit or explicit relationships between them
(bordes), whether or not directionality exists in the relationship. Given its popularity and
openness to data collection, Twitter is one such complex system in which we can observe
relationships through various indicators: number of friend/follower connections, retweets,
and mentions (Del-Fresno-García, 2014). These relations can be analyzed from two general
perspectives by focusing on social relations between individuals through established follow-up
connections (taking account of the double, bidirectional follower/friend perspective) y el
information network based on tweet-produced interaction (miers, Sharma et al., 2014). El
objective is to describe a given community’s underlying network and analyze the existing

2 https://blogs.ec.europa.eu/eupolicylab/about-us/.

Estudios de ciencias cuantitativas

426

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
q
s
s
/
a
r
t
i
C
mi
–
pag
d

F
/

4
2
4
2
3
2
1
3
6
4
3
3
q
s
s
_
a
_
0
0
2
4
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

W.
h
oh

i
norte
F
yo
tu
mi
norte
C
mi
s

pag
oh
yo
i
C
y

yo
a
b
s

i
norte

t
h
mi

mi
tu
r
oh
pag
mi
a
norte
Ud.
norte
i
oh
norte
?

Mesa 1.

Twitter profiles of influencers identified in the EC Report

Policy lab [Twitter user]
Demos Helsinki [DemosHelsinki]

EU Forum Alpbach [forumalpbach]

iMinds [imec_int]

FutureGov [FutureGov]

Governance International [govint_org]

iNetwork [theinetwork]

Country

Finland

Austria

Bélgica

Reino Unido

La 27e Région [La27eregion]

Francia

Laboratorio per l’innovazione

[LabInnovazione]

LabGov [LabGov]

Localis [Localis]

Nesta [nesta_uk]

Italia

Reino Unido

OECD Observatory for Pub. Serv.

Francia

Inno. [OPSIgov]

Publieke Waarden [Publiekewaarden]

Países Bajos

Twitter profile
setup date
2014

2009

2007

2008

2011

2010

2009

2015

2013

2008

2015

2011

Tweets
2,638

4,311

9,193

10,967

3,586

7,057

4,331

5,454

4,276

16,751

1,681

37,180

Followers (# profiles
following
this profile)

8,482

7,056

18,225

22,839

1,821

1,989

7,901

2,977

3,353

103,364

4,161

7,740

Friends (# profiles
seguido por
this profile)
2,453

565

2,322

7,210

2,041

2,202

1,877

103

1,004

569

1,225

4,999

3,164

q
tu
a
norte

t
i
t

t
i
v
mi
S
C
mi
norte
C
mi
S
tu
d
mi
s

4
2
7

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
q
s
s
/
a
r
t
i
C
mi
–
pag
d

F
/

4
2
4
2
3
2
1
3
6
4
3
3
q
s
s
_
a
_
0
0
2
4
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Who influences policy labs in the European Union?

clusters and a range of indicators and statistics. Influence is one of the issues that can be
researched through social network analysis.

Influencers have largely been analyzed in marketing studies, particularly in relation to
redes sociales (Casaló, Flavián, & Ibáñez-Sánchez, 2017; hsu, Chuan-Chuan Lin, & Chiang,
2013; Uzunoğlu & Kip, 2014; zhang & Caverlee, 2019), where digital influencers have a
wider reach among their online social contacts than in traditional offline communication
(Lyons & Henderson, 2005). This is how brands seek to design effective campaigns by using
influencers as a reference group to influence their audience. The identification of relevant
influencers has become a popular research topic in business (kim & Tran, 2013; Liu, Jiang
et al., 2015; Zhu, 2013). Numerous studies try to identify influencers on social media plat-
formas, given their increasing importance as a part of companies’ marketing strategies (Nip &
Fu, 2016; Sol, Wang y cols., 2016).

Shmargad (2018) studied Twitter influencers in politics using retweets as the best indicator
of a given user’s influence, as they capture other users’ willingness to engage with others’ mes-
sages. Centrality has also been studied in the specific case of social media platforms, conclud-
ing that a relationship exists between an individual’s position in a group and their behavior
(Klein, Ahlf, & sharma, 2015). In politics, influence has mainly been studied in relation to
electoral competition (Shmargad & Sanchez, 2020), although generally these studies focus
on a particular event involving a surge in social media communication and not on the network
of relations between institutions (policy labs in our case), where the act of following another
profile can involve recognition in a process of accumulation of social capital (Bourdieu, 1986).

Our study explores the nature of the global public innovation ecosystem taking as its start-
ing point the influencers of European policy labs, defined in the EC report as entities that advo-
cate the creation of policy labs. This influence at an institutional level will be analyzed by
following the different relationships in a given policy lab’s Twitter network.

4. METODOLOGÍA

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
q
s
s
/
a
r
t
i
C
mi
–
pag
d

F
/

4
2
4
2
3
2
1
3
6
4
3
3
q
s
s
_
a
_
0
0
2
4
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

4.1. Sample and Research Design
Our initial sample consisted of 64 policy labs and 13 “influencers,” identified in the EC-
commissioned “Public Policy Labs in European Member States” report. This report has been
taken as a starting point to explore this public innovation ecosystem, given the relevance of the
EU and the importance of the expert reports they commission in terms of agenda-setting and as
foundations for future regulatory decisions.

We identified some problematic policy lab Twitter profiles that have associated Twitter
accounts that may not be exclusively dedicated to lab activity. Labs with descriptions suggest-
ing they are primarily or exclusively used by the policy lab were included. Similarmente, tenemos
included labs with a wider scope if their profiles or tweet content refer to innovation activities
covered by the abovementioned definition (es decir., CityofOdense and AlpesMaritimes). En algunos
casos, we have found that accounts have been redirected (p.ej., UKTIIdeasLab is now redi-
rected to TradeDesignLab—we have included the latter in the present study). Another case
was the UNHCR policy lab, which links globally to the United Nations but was located in
the online Refugee Aid Initiative in Greece report (UNHCRInnovation).

Initial profile identification and data collection took place in spring 2018, finding that 42 de
el 64 labs had a presence on Twitter. A second survey was conducted in September 2019 a
determine any subsequent changes. En esto, we found that MindLabDK, one of the most prom-
inent European policy labs according to Romero-Frías and Arroyo-Machado (2018), had

Estudios de ciencias cuantitativas

428

Who influences policy labs in the European Union?

disappeared following cessation of activities at the end of 2018. Además, PFI Region
Sjælland (PFI_regsj) and Trade Design Lab (TradeDesignLab) no longer existed.

At that point, el 39 policy labs included in the EC report (plus the EC’s own lab) eran
distributed geographically as follows (from highest to lowest number): 13 in the United
Kingdom, eight in France, three each in Denmark, España, and the Netherlands, two each in
Italy and Sweden, y 1 each in Finland, Greece, Irlanda, and Portugal, plus the EU lab.

4.2. Data Processing

We collected information on Twitter related to the labs in the sample (42 en 2018 y 39 en
2019) and to the so-called “influencers”; this including followers, amigos, number of tweets,
descripción, liza, profile set-up date, ubicación, and language.

The data gathered (profiles and interconnections) were collected through scripts pro-
grammed in python using the Tweepy and Twython libraries. We also used R3 to treat some
datos. In our study we used the locations found in the free field offered to Twitter users. Teniendo
collected the locations, we searched OpenStreetMap API to geolocalize the accounts. Finalmente,
we created heat maps using R to indicate the presence of accounts worldwide.

We analyzed the policy lab network by visualizing the connections between labs with the
Gephi3 tool (Bastian, Heymann, & Jacomy, 2009). The graph obtained shows how the sample
labs related to each other and which network nodes were the most relevant. We conducted
this analysis on a global scale, taking account of the network as a whole and paying attention
to each node or lab at local level. At a global level, we calculated the diameter, maximum
eccentricity (greatest distance) between all node pairs, and the mean distance between all of
a ellos. For each node the degree of entry (indegree) and exit (outdegree) have been taken into
cuenta; these are equivalent to account followers and friends, respectivamente. Asimismo, eigen-
vector centrality has been calculated (Bonacich, 1972). This is determined by using an itera-
tive process that takes account of the degree of entry and exit of a node and the quality of these
connections. We also calculated descriptive statistics for the mean and standard deviation (Dakota del Sur)
from follower and friend data.

In total, 41,058 amigos (outdegree) y 843,701 followers (indegree) of policy labs have
been downloaded; of these, 36,338 amigos (potential policy lab influencers) y 786,453
followers are unique. These figures indicate that policy labs attract much more attention from
third parties than they pay to others by following their accounts.

The profile geolocation process revealed that 510,715 Twitter accounts provided locations,
of which 110,156 were unique. Because this is not a standardized field on Twitter, the specific
country to which each account belonged had to be identified using the OpenStreetMap API.
Alguno 73,769 unique locations were correctly identified, donación 487,232 different Twitter
accounts associated with a specific country. De este modo, 77.14% of initial friends and 58.38% de
followers were assigned a location.

4.3. Public Administration Style Differences

Excluding the EU Policy Lab, which is located at Europe level, the remaining 38 laboratories
with a presence on Twitter are distributed by administration style as follows:

(cid:129) Anglo-Saxon (AS) (14): Reino Unido (13) and Ireland (1)

3 R Scripts for data processing are available at GitHub (https://doi.org/10.5281/zenodo.7590866).

Estudios de ciencias cuantitativas

429

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
q
s
s
/
a
r
t
i
C
mi
–
pag
d

F
/

4
2
4
2
3
2
1
3
6
4
3
3
q
s
s
_
a
_
0
0
2
4
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Who influences policy labs in the European Union?

(cid:129) Nordic (NO) (9): Dinamarca (3), Países Bajos (3), Suecia (2), and Finland (1)
(cid:129) Southern Europe (SO) (15): Francia (8), España (3), Italia (2), Greece (1), and Portugal (1)

There is no Germanic policy lab in the sample. All geolocalized Twitter accounts in Europe
were classified by administration style and then visualized through a Sankey diagram to ana-
lyze the location of influencers and the subsequent patterns of policy labs. We grouped policy
labs by administration style and influencers by country. To improve the visualization, countries
representing less than 0.2% of total influencer connections were eliminated. We then estab-
lished the follow-up flows between administration styles and influencers using percentages of
influencers over the total instead of absolute values.

4.4. Content Analysis

We conducted a content analysis of Twitter profiles to classify the top policy lab influencers
identified in our analysis by influencer type and country of origin (Holsti, 1969). Our profile
sample is based on the top 100 influencers of all European policy labs and on the top 50 labs
in each of the three administration styles identified in the study sample. We ranked influencers
by indegree and eigenvector centrality.

An initial taxonomy was explained to two independent research assistants who were asked
to classify part of the sample to test the classification scheme. After sharing results, alguno
adjustments were agreed which led to the establishment of the following influencer types:
Academia; Negocio; Expert (female/male); Government institutions; Media; Networks and
international organizations; Nonprofit organizations; and Others (including conferences,
events, and internet services). We also recorded country profiles. The three researchers then
coded the influencers separately and shared their results, reaching a consensus in the few
cases on which they initially disagreed.

Finalmente, to respond to our research questions, we used a contingency table and correspon-

dence analysis to assess differences between administration styles and influencer type.

5. RESULTADOS

5.1. European Policy Lab and Influencer Network
To answer RQ1, “What kind of network exists between European policy labs and their poten-
tial influencers (their followed profiles or friends)?,” Figure 1 shows the network of policy labs
and influencers; eso es, all outdegree connections (amigos). In some cases this relation is recip-
rocal. From the labs’ perspective, this is reflected by the directions and colors of the edges,
with blue for outdegree connections and red for indegree connections. Policy labs are repre-
sented by blue circles and influencers by red circles. The size of each node is calculated from
the indegree, except for those labs that have been shown as equivalent in size to indegree 7 a
give a homogeneous representation that allows the reader to focus on the influencers—the
object of our study. All lab names have been included together with all influencer names with
indegree 7 or higher. Edge thickness has no specific meaning. To properly visualize the net-
trabajar, only profiles with indegree 2 and belonging to the main component have been taken
into account. The network in Figure 1 consists of 3,096 nodos, including labs and influencers,
y 13,785 bordes.

Cifra 1 identifies two main clusters by size, corresponding mainly to British entities in the
upper part and French entities in the lower part. The United Kingdom cluster comprises UK
labs and the Irish lab (DCCStudio), and is next to the center of the network in closer

Estudios de ciencias cuantitativas

430

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
q
s
s
/
a
r
t
i
C
mi
–
pag
d

F
/

4
2
4
2
3
2
1
3
6
4
3
3
q
s
s
_
a
_
0
0
2
4
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Who influences policy labs in the European Union?

Cifra 1. Network of policy labs and main influencers.

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
q
s
s
/
a
r
t
i
C
mi
–
pag
d

F
/

4
2
4
2
3
2
1
3
6
4
3
3
q
s
s
_
a
_
0
0
2
4
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

connection with other countries. This cluster includes the most relevant influencers, como
indicated by labels on the map. nesta_uk and FutureGov are two of the most prominent influ-
encers, as recognized by the EC report and confirmed by their position on the map. Claramente,
nesta_uk holds a more central position in the European ecosystem than FutureGov, cual es
more central to the British cluster.

Note that PolicyLabUK stands at the core of the British ecosystem—as would be expected
given its central role within the UK government’s Cabinet Office—which implies that it is
situated at a very high level in the administration. Influencers detected in this cluster include
British government units and departments, such as innovateuk, the UK’s innovation agency
funding ideas in science and technology; cabinetofficeuk, the Prime Minister’s office;
UKParliament; hmtreasury, the UK Government’s economic and finance ministry; beisgovuk,
the UK Department for Business, Energy and Industrial Strategy; and mhclg, UK Ministry of
Housing, Communities and Local Government. This shows affiliations to departments that
can act as clients, users, funders, or regulators of lab activities, entre otros roles.

From the nonprofit sector, the Open Data Institute (ODIHQ) is identified as an important
influencer in its work with companies and governments to build an open, trustworthy data
ecosystem. The same stands for designcouncil, an institution focused on design at all levels,
from grassroots to government, developing training programs and research.

Estudios de ciencias cuantitativas

431

Who influences policy labs in the European Union?

Within the same cluster, we find labs of importance in the UK’s devolved regions: iLab_NI,
the public innovation laboratory of Northern Ireland, which was set up in 2014 in the govern-
ment’s Finance Department to innovate in developing public services; ylabwales, a Public
Services Innovation Lab for Wales, which emerged from a partnership between Nesta and
the University of Wales at Cardiff; or creativescots, a public body that supports the arts, pantalla,
and creative industries in Scotland. It also includes Irish profiles, such as DubCityCouncil and
DCCStudio.

The present study reveals the importance of significant individuals as influencers, given the
greater mobility of people across countries and organizations. Within the Anglo-Saxon cluster,
the most significant individuals are Dominic Campbell, CEO of FutureGov, and Andrea
Siodmok, Deputy Director at the Cabinet Office responsible for Policy Innovation across the
UK Government through Policy Lab, the Open Innovation Team, and SKYrooms innovation
spaces. The latter was also connected to designcouncil, an organization committed to improv-
ing life through design and collaboration across institutions. Not directly linked to labs, nosotros
also found the economist Mariana Mazzucato (MazzucatoM), an economist and Director of
the Institute for Innovation and Public Purpose at University College London; Tim Berners-Lee
(timberners_lee), the inventor of the World Wide Web; and Philip Colligan, from the Raspberry
Pi Foundation. The last two are involved in the digital world. Close to the center of the network
we find Christian Bason, ex CEO of Mindlab, the Danish policy lab, and Geoff Mulgan, Chief
Executive of Nesta, the most influential and connected influencer of the other European Labs.

Influencers from the United States are found in near central network positions in this area.
They are the most relevant from countries outside Europe. Central positions are indicative of
shared connections with labs from other European countries, as seen in Figure 2. Two

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
q
s
s
/
a
r
t
i
C
mi
–
pag
d

F
/

4
2
4
2
3
2
1
3
6
4
3
3
q
s
s
_
a
_
0
0
2
4
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Cifra 2. Sankey diagram showing influencers’ countries by administration style. Southern Europe (SO); Anglo-Saxon (AS); Nordic (NO).

Estudios de ciencias cuantitativas

432

Who influences policy labs in the European Union?

academic labs with high social impact are TheGovLab, a lab belonging to New York Univer-
sity, and the Parsons Design for Social Innovation and Sustainability Lab (desisparsons), un
action research laboratory at The New School in New York.

Globally, we can conclude that the Anglo-Saxon cluster includes the more influential pro-
files. Outside of this, other national clusters can be observed. The biggest is the French cluster,
which remains relatively isolated from another cluster made up of various Southern European
and Nordic countries.

The United Nations (UN) and the EC (EU_Commission) are both intergovernmental orga-
nizations that are placed in central positions. The 27e Région (La27eregion) is the most influ-
ential French influencer, as can be seen by its position closer to other European labs and not at
the core of French institutions. It is a public transformation lab for the design of public policies.
openlivinglabs is the Twitter profile of the European Network of Living Labs (ENoLL), an influ-
ential actor in social innovation and cocreation practices across private and public
organizaciones.

Cifra 1 shows how network analysis can be an effective approach to reveal the complex
ecosystem built around European policy labs. As we will examine below, this ecosystem goes
beyond the actor identified in the EC report taken as the starting point for this research.

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
q
s
s
/
a
r
t
i
C
mi
–
pag
d

F
/

4
2
4
2
3
2
1
3
6
4
3
3
q
s
s
_
a
_
0
0
2
4
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

5.2.

Influencers Identified on Twitter

Our RQ2 refers to the profile of influencers on Twitter and whether or not they match those in
the EC report. To answer this, we started with the 39 policy labs with a presence on Twitter
(including the EU Policy Lab). These labs have mean figures of 21,633.4 followers (SD ±
57,345.6), 1,052.8 friend accounts (SD ± 1,064.6), y 7,100.67 published tweets (SD ±
10,218.73). To focus on the most important influencers, we considered profiles followed by
policy labs with a higher indegree—that is, those profiles followed by a larger number of labs.
We used indegree 6, incluido 106 profiles, to examine influencers. This increased the initial
number of 51 profiles (indegree 7), which have been named in Figure 1. The researchers clas-
sified these profiles by country and organization type.

The data show that a majority of profiles (57%) are located in the United Kingdom,
followed by France (16%) y los estados unidos (12%). Alguno 5% have a European reach
and another 5% a global reach. The other countries in the sample with 1% o 2% are Australia,
Canada, Dinamarca, Irlanda, y alemania. Del 106 profiles, all use English in their descrip-
ciones, except the French accounts.

The distribution by profile type is government institutions (32%), experts (20%; of which
solo 29% are women), and nonprofit organizations (18%). We found a lower presence for
negocio (8%), the media (7%), networks and international organizations (6%), academia
(3%), y otros (8%), which included heterogeneous profiles such as conferences, events,
and internet services.

Del 106 most-followed profiles, only three are influencers identified in the EC report:
Nesta, FutureGov, and La 27e Région—although this rises to five if we incorporate people
directly linked to organizations as indicated above. Mesa 2 shows descriptive data for the
Twitter profiles of the 15 main influencers detected. The table also shows the evolution of
rank-order positions over 1 year to determine their stability in time. Indegree was quite stable
with a slight general fall, but eigenvector centrality provides more information. cristiano
Bason, former CEO of Mindlab, lost influence, probably due to the closure of the lab, cual
no longer appears in the list because its Twitter profile was removed. Hootsuite has a very low

Estudios de ciencias cuantitativas

433

Mesa 2. Most relevant influencers according to Twitter

Influencers [Twitter user]
Nesta* [nesta_uk]

Innovate UK*** [innovateuk]

FutureGov* [FutureGov]

Country
Reino Unido

Reino Unido

Christian Bason** [christianbason]

Dinamarca

BBC Breaking News*** [BBCBreaking]

The GovLab*** [TheGovLab]

La 27e Région* [La27eregion]

Reino Unido

EE.UU

Francia

Open Gov Partnership*** [opengovpart]

EE.UU

Tim Berners-Lee*** [timberners_lee]

Dominic Campbell**
[dominiccampbell]

Andrea Siodmok** [AndreaSiodmok]

Open Data Institute*** [ODIHQ]

IDEO*** [ideo]

Geoff Mulgan** [geoffmulgan]

Reino Unido

EE.UU

Reino Unido

Hootsuite*** [hootsuite]

Canada

Data collected in July 2018 and September 2019.

* Influencers included in the report.

** People linked to the influencers in the report.

*** Influencers detected through policy labs’ Twitter friends.

Year Twitter
profile set up
2008

2009

2008

2009

2007

2012

2009

2011

2009

2007

2011

2012

2009

2008

Followers
103,349

Friends

1,225

116,842

32,678

Tweets
16,750

54,076

10,967

10,143

22,838

7,691

35,897

40,536,397

7,300

4,330

27,007

19,377

7,897

61,493

976

332,661

7,211

2,049

1,314

1,877

2,590

553

163,912

21,097

10,255

10,508

14,451

8,840

2,226

7,397

55,518

365,349

17,229

7,941

1,278

5,258

514

69,320

7,854,577

1,493,067

W.
h
oh

i
norte
F
yo
tu
mi
norte
C
mi
s

pag
oh
yo
i
C
y

yo
a
b
s

i
norte

t
h
mi

mi
tu
r
oh
pag
mi
a
norte
Ud.
norte
i
oh
norte
?

Indegree
2019|2018
14|15

Eigenvector
2019|2018
0.75|0.83

11|11

11|12

11|9

11|13

11|12

10|12

9|9

9|11

9|9

9|10

9|9

1|0.65

0.86|0.69

0.82|1

0.74|0.2

0.63|0.8

0.61|0.69

0.91|0.73

0.89|0.55

0.82|0.85

0.8|0.89

0.61|0.44

0.61|0.85

0.61|0.8

0.16|0.09

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
q
s
s
/
a
r
t
i
C
mi
–
pag
d

F
/

4
2
4
2
3
2
1
3
6
4
3
3
q
s
s
_
a
_
0
0
2
4
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

q
tu
a
norte

t
i
t

t
i
v
mi
S
C
mi
norte
C
mi
S
tu
d
mi
s

4
3
4

Who influences policy labs in the European Union?

Mesa 3. Descriptive indicator by policy lab administration style

Tweets

Followers

Friends

Anglo-Saxon

Significar
7,605.4

Dakota del Sur
±9,042.5

Significar
42,163.2

Dakota del Sur
±92,262.7

Significar
1,457.3

Dakota del Sur
±1174.5

Nordic

3,740.3

±7,547.5

7,653.3

±13,486.6

742.4

±670.6

Southern Europe

8,009.8

±12,651.3

10,099.8

±14,789.8

All policy labs

6,849.6

±10,233.3

21,333.3

±58,084.5

634

963

±541.4

±917.1

eigenvector, indicative of the fact that, despite being followed by many policy labs, its impor-
tance in the community is nil as it does not belong to this group. Its presence is probably
explained by the policy of promoting account-following among Twitter users, which could
happen if labs use the tool to manage social networks. The majority have been active for
10 or more years.

To refine our analysis of influencers, in the next section we will segment influencers accord-

ing to the public administration styles of the labs.

5.3. Policy Labs and Influencers’ Public Administration Style

RQ3 focuses on the distribution of policy labs and influencers in relation to their public admin-
istration style. According to their administration style, del 38 policy labs in the sample
(excluding the EU Policy Lab), 14 are AS, nine NO and 15 SO. Mesa 3 shows descriptive
indicators of tweets, followers, and friends (mean and SD), of the policy labs classified by
administration style. Mean figures for publications relating to AS and SO labs are significantly
higher than those of NO labs. The AS labs have more followers (mean = 42,163.2) than either
of the other two.

Mesa 4 shows the number of influencers (columna 1) in the present study by public admin-
istration style and details those that we have geolocalized (columna 2). To limit the total number
of countries and facilitate data visualization, we have selected only those countries with at
el menos 0.2% of all geolocalized influencers, hence eliminating some Twitter profiles (columna 3).

We used a Sankey diagram (Cifra 2) to explore the worldwide distribution of influencers
and determine which public administration style they belonged to and which labs followed
a ellos. Alguno 92.1% of all connections from the AS administration style are included in the
graph. This means that 7.9% of the connections correspond to influencers in countries that

Mesa 4.

Influencers included in the analysis

Anglo-Saxon

Nordic

Southern European

All policy labs

Total influencers (amigos)
18,175

6,428

8,431

36,338

Todo
14,398

4,767

6,545

28,032

Geolocalized influencers

In countries with more than 0.2% of total connections
13,263

4,430

5,803

23,122

Estudios de ciencias cuantitativas

435

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
q
s
s
/
a
r
t
i
C
mi
–
pag
d

F
/

4
2
4
2
3
2
1
3
6
4
3
3
q
s
s
_
a
_
0
0
2
4
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Who influences policy labs in the European Union?

are below the 0.2% limit of total connections. In the SO group, this amounts to 88.7%, cual
indicates a higher number of connections missing in the graph given that they follow accounts
in underrepresented countries. In the sample, 51.6% of all influencers are followed by AS
policy labs, 22.6% by SO labs, y 17.2% by NO labs.

The right-hand side of the graph indicates the countries where the influencers are located:
The first number is the percentage of total influencers of the administration style to which the
country belongs; the second, the percentage of influencers of a country over the total number
of influencers in the sample. Por ejemplo, 57.1% of all influencers of SO policy labs are
French and this represents 14.9% of all influencers in the sample.

Mesa 5 shows the percentage of influencers by country and administration style. The top
tres (Reino Unido, 43.83%; Francia, 14.86%; Países Bajos, 7.6%) correspond to countries
from the three administration styles of the policy labs in the sample. The fourth position is held
by a non-European country (United States, 7.54%). There are only two Germanic administra-
tion style countries (Suiza, 0.86%; Alemania, 0.62%), with very low representation. El
majority of influencers are AS (60.79%), followed by SO (20.94%), NO (16.65%), y
Germanic (1.62%).

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
q
s
s
/
a
r
t
i
C
mi
–
pag
d

F
/

4
2
4
2
3
2
1
3
6
4
3
3
q
s
s
_
a
_
0
0
2
4
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Mesa 5.

Influencers by country and administration style

Country
Reino Unido

Francia

Países Bajos

United States

Finland

España

Canada

Dinamarca

Suecia

Irlanda

Bélgica

Suiza

Australia

Alemania

Italia

República Dominicana

Total

Administration style
Anglo-Saxon

Southern European

Nordic

Anglo-Saxon

Nordic

Southern European

Anglo-Saxon

Nordic

Anglo-Saxon

Southern European

Germanic

Anglo-Saxon

Germanic

Southern European

Influencers (%)
43.83

14.86

7.6

7.54

4.17

2.59

2.32

2.15

1.27

1.1

0.86

0.67

0.62

0.33

0.32

91.23

Alguno 8.77% correspond to countries with less than 0.2% of all influencers and therefore are not included in the
sample.

Estudios de ciencias cuantitativas

436

Who influences policy labs in the European Union?

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

Cifra 3. Geographic distribution of influencers around the world.

Cifra 3 shows the worldwide distribution of influencers (amigos). There are of 28,032
unique influencers in 170 countries; due to the wide range of values, this figure is presented
using a base 10 logarithmic scale. The results show that from a European perspective the ref-
erence organizations are located almost exclusively in the Western world: Europe and North
America.

mi
d
tu
q
s
s
/
a
r
t
i
C
mi
–
pag
d

F
/

4
2
4
2
3
2
1
3
6
4
3
3
q
s
s
_
a
_
0
0
2
4
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

6. DISCUSSION AND CONCLUSIONS

This study uses Twitter to explore the public innovation ecosystem of European policy labs to
gain a better understanding of public innovation communities and the extent to which they are
connected to the different public administration styles. As the OECD (2019, pag. 13) indicates,
the need and the potential for innovation in the public sector is greater than ever, due to a
redefinition of the relationship between governments and their constituents using instruments
such as internal innovation labs (Mergel, Gong, & Bertot, 2018). We took as our starting point
a report commissioned by the EC to identify 64 laboratories and their main influencers. Given
the linguistic and national diversity and the different administrative styles within the EU, SNA
helps to shed light on this complex innovation ecosystem.

Cifra 1 confirms the existence of national ecosystems integrated in a global network, en
which actors corresponding to the AS sphere, particularly the United Kingdom, have a special
influencia. The highest number of all the influencers detected is from the United Kingdom
(43.8%). Together with 7.7% from the United States, this means that more than 50% del
influencers detected correspond to entities from countries with an AS administration style.
Focusing on the top 106, the figure rises to 69% for these two countries. The AS style is marked
by greater adoption of private sector practices, which constitute the breeding ground for
innovation laboratories, favoring the adoption of policy labs and institutions linked to public
innovation. This is confirmed by the influence in terms of both number and relevance of the

Estudios de ciencias cuantitativas

437

Who influences policy labs in the European Union?

AS profiles in our study. Además, despite the fact that actors in the network tend to follow
profiles from their own country and culture, the largest number of followings outside their
environment are with entities from countries with an AS administrative style. Given that influ-
encers act as important connectors to generate and disseminate innovations, there is a result-
ing globalization of AS administrative practices through the proliferation of policy labs. Como
shown by previous studies applied to the municipal sphere and topics, such as e-government
and public innovation (Bolívar, 2018; Bonsón & Bednárová, 2018), administrative style can
explain differences between countries. This strong predominance of AS influencers reinforces
our earlier view and could be explained by the fact that public innovation delivered by policy
labs is more closely aligned to features of administration style, such as openness to public
sector reforms, high transparency, and citizen engagement (Torres, 2004).

The influence ecosystem revealed by SNA using Twitter data is different and more complex
than the results of the EC report, confirming the validity of this method to supplement opinions
offered solely by experts. Another important difference is the percentage of AS influencers:
38% in the EC report and 61% according to Twitter. This strong influence, exerted especially
by the United Kingdom, needs to be considered in relation to the consequences of Brexit. Él
means that most policy lab influencers and, por extensión, public innovation influencers as a
entero, are currently outside the EU.

The network in Figure 1 shows how some institutional profiles such as those of the UN and
the EC are located in very central positions, connecting the two main clusters (United
Kingdom and France) and other smaller clusters such as those of southern countries (España
and Italy) and northern countries (Países Bajos, Dinamarca, and Sweden). The network offers
opportunities to develop a more powerful and interconnected network of policy labs that
boost public innovation in the EU. If the intention is for public innovation through policy labs
to be based on collaboration, more institutional efforts are needed to coordinate and exchange
best practices and projects across different countries and administrative styles.

Only three influencers (Nesta, FutureGov, and La 27e Région) out of 13 are identified as
“big nodes” in the network. Sin embargo, within the top 15 influencers we did identify related
profiles of individuals (p.ej., Geoff Mulgan and Dominic Campbell). The presence of experts
is very significant among top influencers: 21 out of 106 (20%). A certain degree of mobility of
experts across countries and organizations has also been observed. Por ejemplo, after the
closure of the Danish Mindlab, former CEO Christian Bason occupied a highly relevant
position with the Design Center project, and Jesper Christiansen moved from MindLab to
Nesta. This indicates that influence should be understood not only at an institutional level,
but also based on personal relations within a dynamic and changing ecosystem. Policy lab
practices may foster an emulation effect (Tõnurist et al., 2017), which also contributes to
the flow and exchange of people. Finalmente, in relation to the experts a gender bias clearly exists,
as only 29% are women.

Digital culture is quite strongly connected to policy lab influencers, as illustrated by the
significant position within the network of Tim Berners-Lee, the creator of the World Wide
Web, and by the types of activity conducted by organizations such as Nesta. Digital culture
and innovation practices have grown in parallel in recent decades. The online presence of
laboratories is undoubtedly important given their interest in promoting participation and inno-
vation in the development of public policies, including the adoption of tools and strategies
typical of Web 2.0 (Nam, 2012). De hecho, one of the policy recommendations is the importance
of generating an open digital communication policy that maximizes the impact of the entity’s
comportamiento, both nationally and internationally.

Estudios de ciencias cuantitativas

438

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
q
s
s
/
a
r
t
i
C
mi
–
pag
d

F
/

4
2
4
2
3
2
1
3
6
4
3
3
q
s
s
_
a
_
0
0
2
4
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Who influences policy labs in the European Union?

At a national level, regardless of the level of decentralization, there is a clear imbalance in
the development of policy labs in Europe. A more proactive policy is needed in the field of
public innovation to institutionalize venues and spaces for innovation. At the EU level, the EC
has an opportunity to promote and lead an ecosystem, coordinating national efforts to develop
a model that is compatible with the various administrative cultures of the member states. En esto
sense, the EC should also review methodologies such as SNA for the identification of commu-
niidades. This study could offer exploratory methods to expand the identification process by using
hybrid approaches based on network and content analysis.

Finalmente, it should be noted that as can be seen in Figure 3, from a European perspective,
influencers are located almost exclusively in Europe and North America. This shows that there
are opportunities to open the focus to other regions, such as South America, que tiene un
vibrant innovation ecosystem, thereby fostering international cooperation to learn from initia-
tives in other countries and transfer good practices developed in Europe.

6.1. Limitations and Future Research

We should acknowledge some limitations of the present study. Our study focused on the
EC report on policy labs, which in itself contained the aforementioned limitations in terms
of its identification of policy labs and their representativeness. The report was an ade-
quate starting point to initiate this line of research, but recognizing these limitations should
help us understand some of the results—for example, the clear bias towards a strong AS
presence and a weak Germanic presence. The use of English as the lingua franca of inter-
national communications is also a factor that needs to be considered when interpreting
the results. Además, the use of visualization techniques to balance the relevance of
information and its comprehension has led us to take decisions that might differ in other
casos.

As we have explained in our results in relation to RQ3, given that only countries with at
el menos 0.2% of total influencers were included in this analysis, the opportunity exists to
explore influencers on a smaller scale in specific countries. Por ejemplo, 11.34% of poten-
tial influencers with the SO administration style were excluded from more detailed analysis.
The aggregated distribution of some of these influencers is shown in the map in Figure 3.
This opens up opportunities for future research aimed at discovering influencers in linguistic
areas of influence, for example of Spanish in Latin America. Además, the methodology
used in this paper could be adapted to discover new policy labs or influencers at EU level or
in countries such as Australia or the Dominican Republic, which already have a strong pres-
ence in our sample.

Para concluir, SNA seems to be an appropriate methodology to decipher the intricacies of
public innovation ecosystems, given that they are network-like models of public governance
(Scupola & Zanfei, 2016). Similarmente, in our view it is an adequate approach to address the lack
of knowledge regarding Transnational Public Sector Knowledge Networks (Dawes & Gharawi,
2018). The results of this study show the promising applications of this method to advance
knowledge of emergent topics such as public innovation, which potentially involves partici-
pation by all citizens and organizations.

EXPRESIONES DE GRATITUD

The authors would like to thank Gaspar Noé and Ingmar Bergman for their inspiring and emo-
tionally supportive work.

Estudios de ciencias cuantitativas

439

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
q
s
s
/
a
r
t
i
C
mi
–
pag
d

F
/

4
2
4
2
3
2
1
3
6
4
3
3
q
s
s
_
a
_
0
0
2
4
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Who influences policy labs in the European Union?

CONTRIBUCIONES DE AUTOR

Esteban Romero-Frías: Conceptualización, Metodología, Administración de proyecto, Supervisión,
Escritura: borrador original, Escritura: revisión & edición. Daniel Torres-Salinas: Adquisición de financiación,
Recursos, Validación, Escritura: revisión & edición. Wenceslao Arroyo-Machado: Curación de datos,
Análisis formal, Investigación, Software, Visualización.

CONFLICTO DE INTERESES

Los autores no tienen intereses en competencia.

INFORMACIÓN DE FINANCIACIÓN

This work was funded by the Spanish Ministry of Science and Innovation grant numbers
PID2019-109127RB-I00/SRA/10.13039/501100011033 and PID2020-117007RA-I00, y
Regional Government of Andalusia Junta de Andalucía grant number A-SEJ-638-UGR20.
Wenceslao Arroyo-Machado has an FPU Grant (FPU18/05835) from the Spanish Ministry of
Universities. Daniel Torres-Salinas is supported by the Reincorporation Programme for Young
Researchers from the University of Granada.

DISPONIBILIDAD DE DATOS

R Scripts for data processing and Twitter data (user IDs) are available at https://doi.org/10.5281
/zenodo.7590866.

REFERENCIAS

Bastian, METRO., Heymann, S., & Jacomy, METRO. (2009). Gephi: An open
source software for exploring and manipulating networks. Pro-
ceedings of the International AAAI Conference on Web and Social
Media, 3(1), 361–362. https://doi.org/10.1609/icwsm.v3i1.13937
Bolívar, METRO. PAG. R. (2018). Creative citizenship: The new wave for
collaborative environments in smart cities. Academia Revista
Latinoamericana de Administración, 31(1), 277–302. https://doi
.org/10.1108/ARLA-04-2017-0133

Bonacich, PAG. (1972). Factoring and weighting approaches to status
scores and clique identification. Journal of Mathematical Sociol-
ogia, 2(1), 113–120. https://doi.org/10.1080/0022250X.1972
.9989806

Bonsón, MI., & Bednárová, METRO. (2018). The use of YouTube in western
European municipalities. Government Information Quarterly,
35(2), 223–232. https://doi.org/10.1016/j.giq.2018.04.001

Bourdieu, PAG. (1986). The forms of capital. In J. Richardson (Ed.),
Handbook of theory and research for the sociology of education
(páginas. 241–258). madera verde.

Casaló, l. v., Flavián, C., & Ibáñez-Sánchez, S. (2017). Antecedents
of consumer intention to follow and recommend an Instagram
cuenta. Online Information Review, 41(7), 1046–1063. https://
doi.org/10.1108/OIR-09-2016-0253

Dawes, S. S., & Gharawi, METRO. A. (2018). Transnational public sector
knowledge networks: A comparative study of contextual dis-
tancias. Government Information Quarterly, 35(2), 184–194.
https://doi.org/10.1016/j.giq.2018.02.002

Del-Fresno-García, METRO. (2014). Haciendo visible lo invisible: Visua-
lización de la estructura de las relaciones en red en Twitter por
medio del análisis de redes sociales. El Profesional de La Informa-
ción, 23(3), 246–252. https://doi.org/10.3145/epi.2014.may.04
Dwivedi, oh. PAG. (2005). Administrative culture and values: Enfoques.

In Administrative culture in a global context (páginas. 19–36).

Batán, METRO., & Lochard, A. (2016). Public policy labs in European
Union member states. Luxembourg: Publications Office of the
European Union. https://doi.org/10.2788/799175

Gieske, h., Jorge, B., van Meerkerk, I., & van Buuren, A. (2020).
Innovating and optimizing in public organizations: Does more
become less? Public Management Review, 22(4), 475–497.
https://doi.org/10.1080/14719037.2019.1588356

Hartley, j. (2005). Innovation in governance and public services:
Past and present. Public Money & Management, 25(1), 27–34.
https://doi.org/10.1111/j.1467-9302.2005.00447.x

Hjelmar, Ud.. (2019). The institutionalization of public sector innova-
ción. Public Management Review, 23(1), 53–69. https://doi.org
/10.1080/14719037.2019.1665702

Holsti, oh. R. (1969). Content analysis for the social sciences and

humanidades. Addison-Wesley.

hsu, C., Chuan-Chuan Lin, J., & Chiang, h. (2013). The effects of
blogger recommendations on customers’ online shopping inten-
ciones. Internet Research, 23(1), 69–88. https://doi.org/10.1108
/10662241311295782

kim, Y. S., & Tran, V. l. (2013). Assessing the ripple effects of online
opinion leaders with trust and distrust metrics. Expert Systems
with Applications, 40(9), 3500–3511. https://doi.org/10.1016/j
.eswa.2012.12.058

Klein, A., Ahlf, h., & sharma, V. (2015). Social activity and structural
centrality in online social networks. Telematics and Informatics,
32(2), 321–332. https://doi.org/10.1016/j.tele.2014.09.008

Luis, t. GRAMO. (2008). Network science: Theory and practice. Chich-

ester: John Wiley & Sons.

Luis, j. METRO., McGann, METRO., & Blomkamp, mi. (2020). When design
meets power: Design thinking, public sector innovation and the
politics of policymaking. Política & Política, 48(1), 111–130.
https://doi.org/10.1332/030557319X15579230420081

Estudios de ciencias cuantitativas

440

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
q
s
s
/
a
r
t
i
C
mi
–
pag
d

F
/

4
2
4
2
3
2
1
3
6
4
3
3
q
s
s
_
a
_
0
0
2
4
7
pag
d

b
y
gramo
tu
mi
s
t

oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Who influences policy labs in the European Union?

Liu, S., Jiang, C., lin, Z., Ding, y., Duan, r., & Xu, z. (2015). Identify-
ing effective influencers based on trust for electronic word-of-mouth
marketing: A domain-aware approach. Information Sciences, 306,
34–52. https://doi.org/10.1016/j.ins.2015.01.034

Lyons, B., & Henderson, k. (2005). Opinion leadership in a
computer-mediated environment. Journal of Consumer Behav-
iour, 4(5), 319–329. https://doi.org/10.1002/cb.22

McGann, METRO., Blomkamp, MI., & Luis, j. METRO. (2018). The rise of
public sector innovation labs: Experiments in design thinking
for policy. Policy Sciences, 51(3), 249–267. https://doi.org/10
.1007/s11077-018-9315-7

Mergel, I., Gong, y., & Bertot, j. (2018). Agile government: Sistema-
atic literature review and future research. Government Informa-
tion Quarterly, 35(2), 291–298. https://doi.org/10.1016/j.giq
.2018.04.003

moore, METRO. h. (2005). Break-through innovations and continuous
mejora: Two different models of innovative processes in
the public sector. Public Money & Management, 25(1), 43–50.
https://doi.org/10.1111/j.1467-9302.2005.00449.x

miers, S. A., sharma, A., Gupta, PAG., & lin, j. (2014). Información
network or social network? The structure of the Twitter follow
graph. In Proceedings of the 23rd International Conference on
World Wide Web (páginas. 493–498). https://doi.org/10.1145
/2567948.2576939

Nam, t. (2012). Suggesting frameworks of citizen-sourcing via
Government 2.0. Government Information Quarterly, 29(1),
12–20. https://doi.org/10.1016/j.giq.2011.07.005

Nip, j. Y. METRO., & Fu, k. (2016). Challenging official propaganda?
Public opinion leaders on Sina Weibo. The China Quarterly,
225, 122–144. https://doi.org/10.1017/S0305741015001654
OECD. (2017). Systems approaches to public sector challenges:
Working with change. París: OECD Publishing. https://doi.org
/10.1787/9789264279865-en

OECD. (2019). Embracing innovation in government: Global trends
2019. París: OECD Publishing. https://www.oecd.org/innovation
/innovative-government/embracing-innovation-in-government
-global-trends-2019.htm

Olejniczak, K., Borkowska-Waszak, S., Domaradzka-Widła, A., &
Parque, Y. (2020). Policy labs: The next frontier of policy design and
evaluación? Política & Política, 48(1), 89–110. https://doi.org/10
.1332/030557319X15579230420108

Olejniczak, K., Newcomer, K., & Borkowska-Waszak, S. (2016).
Policy labs: The next frontier of policy design and evaluation.
Implications for the EU Cohesion Policy. Posada. Dotti (Ed.),
Learning from implementation and evaluation of the EU cohesion
política: Lessons from a research-policy dialogue (páginas. 224–241).
RSA Research Network on Cohesion Policy.

Osborne, S. PAG. (Ed.). (2010). The new public governance? Emerging
perspectives on the theory and practice of public governance.
Londres: Routledge. https://doi.org/10.4324/9780203861684
Painter, METRO., & Peters, B. GRAMO. (2010). The analysis of administrative
traditions. En m. Painter & B. GRAMO. Peters (Editores.), Tradition and public
administración (páginas. 3-dieciséis). Palgrave Macmillan UK. https://doi
.org/10.1057/9780230289635_1

Pina, v., Torres, l., & Royo, S. (2007). Are ICTs improving trans-
parency and accountability in the EU regional and local gov-
ernments? An empirical study. Public Administration, 85(2),
449–472. https://doi.org/10.1111/j.1467-9299.2007.00654.x
Pollitt, C., & Bouckaert, GRAMO. (2011). Public management reform:
A comparative analysis – new public management,

governance, and the neo-Weberian state. Oxford: Oxford Uni-
versity Press.

Romero-Frías, MI., & Arroyo-Machado, W.. (2018). Policy labs in
Europa: Political innovation, structure and content analysis on
Twitter. El Profesional de La Información, 27(6), 1181–1192.
https://doi.org/10.3145/epi.2018.nov.02

Royo, S., Yetano, A., & Acerete, B. (2014). E-participation and envi-
ronmental protection: Are local governments really committed?
Public Administration Review, 74(1), 87–98. https://doi.org/10
.1111/puar.12156

Scupola, A., & Zanfei, A. (2016). Governance and innovation in
public sector services: The case of the digital library. Government
Information Quarterly, 33(2), 237–249. https://doi.org/10.1016/j
.giq.2016.04.005

Shmargad, Y. (2018). Twitter influencers in the 2016 US Congres-
sional races. Journal of Political Marketing, 21(1), 23–40. https://
doi.org/10.1080/15377857.2018.1513385

Shmargad, y., & Sanchez, l. (2020). Social media influence and
electoral competition. Social Science Computer Review, 40(1),
4–23. https://doi.org/10.1177/0894439320906803

Sørensen, MI., & Torfing, j. (2011). Enhancing collaborative inno-
vation in the public sector. Administration & Sociedad, 43(8),
842–868. https://doi.org/10.1177/0095399711418768

Sol, P., Wang, NORTE., zhou, y., & luo, z. (2016). Identification of
influential online social network users based on multi-features.
International Journal of Pattern Recognition and Artificial Intelligence,
30(6), 1659015. https://doi.org/10.1142/S0218001416590151

Tõnurist, PAG., Kattel, r., & Lember, V. (2017). Innovation labs in the
public sector: What they are and what they do? Public Manage-
ment Review, 19(10), 1455–1479. https://doi.org/10.1080
/14719037.2017.1287939

Torfing, J., andersen, l. B., Klausen, k. K., & Greve, C. (2020). Pub-
lic governance paradigms. Competing and co-existing. Chelten-
ham: Edward Elgar. https://doi.org/10.4337/9781788971225
Torres, l. (2004). Trajectories in public administration reforms in
European Continental countries. Australian Journal of Public
Administration, 63(3), 99–112. https://doi.org/10.1111/j.1467
-8500.2004.00394.X

Uzunoğlu, MI., & Kip, S. METRO. (2014). Brand communication through
digital influencers: Leveraging blogger engagement. Internacional
Journal of Information Management, 34(5), 592–602. https://doi
.org/10.1016/j.ijinfomgt.2014.04.007

van Buuren, A., Luis, J., Peter Peters, B., & Voorberg, W.. (2020).
Improving public policy and administration: Exploring the poten-
tial of design. Política & Política, 48(1), 3–19. https://doi.org/10
.1332/030557319X15579230420063

Voorberg, w., Bekkers, v., & Tummers, l. (2011). Embarking on the
social innovation journey: A systematic review regarding the
potential of co-creation with citizens. International Research
Society for Public Management (IRSPM), Prague, Czech Repub-
lic. https://repub.eur.nl/pub/39573/

zhang, y., & Caverlee, j. (2019). Instagrammers, fashionistas, y
a mí: Recurrent fashion recommendation with implicit visual influ-
ence. In Proceedings of the 28th ACM International Conference
on Information and Knowledge Management (páginas. 1583–1592).
https://doi.org/10.1145/3357384.3358042

Zhu, z. (2013). Discovering the influential users oriented to viral
marketing based on online social networks. Physica A: Statistical
Mechanics and Its Applications, 392(16), 3459–3469. https://doi
.org/10.1016/j.physa.2013.03.035

Estudios de ciencias cuantitativas

441

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

mi
d
tu
q
s
s
/
a
r
t
i
C
mi
–
pag
d