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Online platforms for smart specialisation strategies and smart growth
Anastasia Panori
Intelspace SA, Thessaloniki, Greece
apanori@intelspace.eu
Margarita Angelidou
Intelspace SA, Thessaloniki, Greece
mangelidou@urenio.org
Luca Mora
Edinburgh Napier University, Edinburgh, UK
l.mora@napier.ac.uk
Alasdair Reid
Edinburgh Napier University, Edinburgh, UK
al.reid@napier.ac.uk
Elena Sefertzi
URENIO Research, Aristotle University of Thessaloniki, Greece
esef@urenio.org
Abstract
This paper reports on the findings of the Online-S3 project, funded under the Horizon 2020
Programme (ISSI-4-2015), which tries to address the challenge of strengthening regional smart growth
policies by developing an online platform for policy advice. The Online-S3 Platform offers a web-
based environment for supporting the design, implementation and assessment of Research and
Innovation Strategies for Smart Specialisation (RIS3) aiming to enrich the methodological framework
that is being used towards enhancing smart growth policy design processes in EU regions. The paper
first provides an overview of the Online-S3 platform, and then, focuses on the applications that could
be used to help regional and national authorities during the priority setting phase of a RIS3 strategic
planning process. Given that this phase relates to the identification and selection of specific sectors that
can be used as flagships to support regional growth, the Online-S3 Platform offers a great tool towards
enhancing the effectiveness of the smart growth paradigm.
Keywords: Online platforms, Regional innovation, Regional economy, Smart specialisation, RIS3,
European Union, Entrepreneurial discovery process.
1. Introduction
Policy design is an inherently complex activity that ordinarily involves multiple stakeholders and
a plethora of insufficient information. Two features that have been identified as essential for improving
strategy formulation processes are extended quantitative analytical exercises and enhanced
stakeholders’ participation (Rowe and Frewer, 2004; Charalabidis et al, 2010; Komninos et al, 2014a;
Panori et al, 2016). During the last decade, data collection and analysis have emerged as two of the
most valuable assets, not only for entrepreneurs concerned with leveraging new market opportunities,
but also for regions, which are required to design innovative strategies for strengthening their economic
growth models. At the same time, the emergence of digital platforms as an intrinsic feature of the
continuously evolving economic structure, has opened new opportunities that relate to issues
concerning stakeholder participation and the exploitation of sophisticated data. Platforms offer cyber-
spaces which enable the formation of new ecosystems, where users can effectively collaborate across a
broad range of activities (Oskam and Boswijk, 2016; Kenney and Zysman, 2016 Biber et al, 2017). In
addition, platform environment can also be exploited in terms of lessening transaction costs,
preempting market failures and reducing instances of incomplete data (Parker et al, 2016).
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The emergence of the platform economy was initially characterized as a “disruptive innovation”
phenomenon, as a result of the re-interpretation of traditional socio-technical and business practices.
However, the rise of the platform capitalism has created important challenges, mainly due to the
creation of new forms of digital economic circulation. An example of this, could be idle assets
exchange by geographically dispersed areas, which are connected through online communities, despite
the absence of any geographic proximity (Langley and Leyshon, 2017). In this changing scenario,
platforms have become a new type of “mediator” for socio-technical and business arrangements,
affecting a broad extend of already existing capitalization processes. Thus, the platform economy has
resulted in a re-organization of economic activity, specifically by resetting entry barriers and changing
the logic of value creation (Kenney and Zysman, 2016).
Whilst various characteristics can be used for platform typology, some of the most prevailing
platform types include: i) platforms for platforms; ii) platforms that provide online digital tools and
support the creation of other platforms and market places; iii) platforms mediating work; iv) retail
platforms; and v) service-providing platforms (Kenney and Zysman, 2016). In most of the above-
mentioned categories, platforms act as knowledge operators (Langlois and Elmer, 2013; van Dijck,
2013; Schwarz, 2016; Angelidou and Psaltoglou, 2017), offering a smart environment for business
processes to be undertaken and promote evidence-based decision-making. Intelligence generated
through data generation and analysis, can lead to entirely new opportunities for those who have access
to the data (Schwarz, 2016; Angelidou, 2017; Panori et al, 2017).
This fast-paced transformation of the digital platform economy, from a “disruptive innovation”
to, what is today, an essential feature of regional economic structures, has had a significant impact
upon policy design in EU member states. Literature on emerging technologies for strategic planning
reveals that the use of platforms as decision-supporting tools has altered existing forms of governance,
strengthened public participation and enabling sophisticated data analysis. The use of platforms for
developing policy design frameworks, specifically intended to foster innovation and smart growth
policies, have not yet been adopted by policy-makers to any great extent (Angelidou et al, 2011;
Komninos, 2014; EC, 2015; Angelidou et al, 2017; Panori et al, 2017). In the case of European Union,
Research and Innovation Strategies for Smart Specialisation (RIS3) have been identified as the primary
policy framework to cultivate smart growth (EC, 2014). In accordance with the official guidance, these
strategies should be formulated by adopting an entrepreneurial discovery process; a practice of
‘choosing races and placing bets’ rather than ‘picking the winners’. Consequently, strategic
interventions should be precise, continuously updated and guided by evidence which is appropriate to
the regional context. At the same time, outcomes should be closely monitored and evaluated, using
both quantitative and qualitative metrics and data.
Situated within these terms of reference, this paper attempts to shed light on the capability of
platforms to reveal opportunities for regional growth. The Online-S3 project, funded under the Horizon
2020 (ISSI-4-2015) programme, directly addresses this challenge by developing an online platform for
policy design. The following sections provide a brief description of the Online-S3 Platform, focusing
mainly on the applications that have been developed to foster regional growth opportunities.
2. The Online-S3 Platform: Applications for regional growth
Given the sharp shift towards an evidence-based policy design framework, EU regions are
required to design and implement RIS3 as an ex ante conditionality in order to receive funding for
research and innovation from the European Regional Development Fund (ERDF). To facilitate and
streamline this process, the European Commission has published a Guide to RIS3 (Foray et al, 2012)
and a handbook for implementing Smart Specialisation Strategies (Gianelle et al, 2016), which taken
together provide a set of methodological steps advising national and regional governments on RIS3
development. Whilst these publications provide valuable resources to facilitate RIS3 design and enable
the subsequent implementation process, the inputs considered are mostly concerned with developing
the methodological framework, and as a result, somewhat neglect the operational directions that could
support the delivery of the methodological tasks in a streamlined and user-friendly way (Reid et al,
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2012; Iacobucci, 2014; Komninos et al, 2014b; Kroll, 2015; Capello and Kroll, 2016; Griniece et al,
2017). Having identified this discrepancy, it would appear that the development of tools for
quantitative analysis during the strategy formation process could be crucial in transforming available
data into intelligence and thus, leverage hidden opportunities for economic growth at a regional level.
The Online S3 Platform which the authors refer to in this paper, is designed to address the current
challenges and shortcomings of RIS3 design, implementation and assessment. The platform itself is a
web-based environment that enables a community of stakeholders to navigate through the six phases of
RIS3 strategic planning. At the same time, the idea of developing an open platform for policy design,
as the Online-S3 Platform, lies on the context of promoting a sharing economy model
1
for smart
growth. Under this context, it offers a peer exchange and learning space that triggers potential
innovative interactions between sectors and regions, which may lead to disruptive synergies, acting as
boosters for smart growth policies. In addition to this, the Online-S3 Platform aims to provide the
necessary means to monitor and assess implementation of RIS3 policies.
By adopting a connected intelligence approach, the Online-S3 Platform uses smart assistance and
roadmaps to: (1) standardize and automate the tasks of strategy elaboration; (2) provide access to
databases guiding strategy formulation; and (3) enable participatory co-design that facilitates the
potential for collaboration amongst various stakeholders. Within the platform ecosystem, co-creation
emerges as a product of effective interaction between users. Data analysis techniques can then be
combined with knowledge co-creation, further illustrating the capability of platforms in policy design
(Angelidou et al, 2012). These features enable the community of stakeholders to interact with each
other, reinforcing a collective intelligence model, that is both creative and effective.
This section presents a group of applications included in the Online-S3 Platform, which have been
developed to support RIS3 development, with the specific intention to exploit underlying growth
opportunities at a regional level. As previously stated, the design of the Platform is based on the premise
of connected intelligence, where the key objective is to provide the user with a helpful guide for
designing a RIS3 strategy, by offering a comprehensive framework of information to guide them through
the process. In acknowledging the existing literature on platform development (Choudary, 2015; Langley
and Leyshon, 2017), the typical structure is based on three overlapping layers: (1) cloud hosting of
applications; (2) online databases; and (3) online collaboration and consultation (Fig.1). These structural
elements are presented to the end user as three main functions (The Guidance, the 29 Applications, and
an online Forum for discussion) that co-exist and co-operate within the Online-S3 Platform.
The Guidance section includes a content management system, offering an introduction to the
RIS3 policy design concept, where some brief descriptions are provided, including What is RIS3? and
How to form a RIS3? A set of external links are also provided, redirecting the user to relevant sites and
supplementary material, such as the JRC’s S3-Platform (http://s3platform.jrc.ec.europa.eu/) and a set of
working papers. Detailed descriptions for each of the 6 phases of the RIS3 development process are
also provided within the Guidance section, which highlight the main linkages and interactions between
them. Furthermore, the forum provides a discussion place for users to interact, including participants
from all components of the quadruple helix, aiming to facilitate and promote collective intelligence
processes throughout the RIS3 strategy design framework (Carayannis and Rakhmatullin, 2014).
Whilst stakeholder participation and guidance are essential components of the Online-S3
Platform, the development of the applications lies at the core of the institutional empowerment that the
platform aims to achieve. Given that each of the 29 applications have been developed as stand-alone
tools, it is not necessary to use the applications in a complete and/or sequential manner. Thus, it is
possible to combine or group them in multiple ways, based on the specific nature of each issue under
consideration; in this way fostering the innovation capacity of the platform (Hargadon, 2003;
Williamson, 2016).
In this paper, the authors have focused on applications that specifically relate to the priority
setting of the RIS3 policy design, a phase that considers emerging growth opportunities aiming to
1
On platform for growth typology, see: http://emeraldinsight.com/doi/full/10.1108/JTF-11-2015-0048.
98
foster regional smart growth in EU regions. Below, a set of four core applications are presented
targeting on revealing emerging sectors of production and helping to identify novel growth
opportunities across the EU regions.
Figure 1: The Online-S3 Platform layers and main functionalities
Application 1: Specialisation Indexes
In analysing existing regional assets, regions should further investigate their key strengths and
advantages, in terms of technological and economic specialisation. This application offers an
opportunity for platform users to explore the three most important categories of regional specialisation;
technological, scientific and economic. In doing so, the user is presented with the following options: i)
to search an existing database to determine the revealed technological advantage (RTA) across various
technological sectors within a region; ii) to establish the scientific profile of a region, based on
publication data obtained from Scopus; and iii) to calculate the regional comparative advantage (RCA),
based on trade flow data that users can upload. Fig.2 illustrates the schematic structure of the
application, including the information flows between the different stages of the application.
Figure 2: Structure of Specialisation Indexes application. Online-S3 Platform (www.s3platform.eu)
Application 2: Related Variety Analysis
Related Variety Analysis is an application that is designed to identify highly correlated sectors
which can represent underlying regional growth opportunities (Fig.3). Related variety refers to the
variety of industries within a region that are cognitively related (Frenken et al, 2007) and maximise the
potential for learning opportunities and growth of existing industries, as well as the local sources of
growth for new industries (Boschma, 2014). As a method for policy making, related variety can be
used to define the level of industrial diversification or the degree to which different industries of an
area share common knowledge bases which allow interaction and knowledge spill-overs to occur.
Combined with other methodologies, related variety facilitates decision making through the
selection of investment priorities for future specialisation. The identification of such sectors is found by
means of combining data from two discrete sources: i) sectoral employment data from EUROSTAT;
and ii) patent data from PATSTAT. The application adopts the following logic: First, the application
reveals the most specialised sectors, in terms of employment and technological specialisation, based on
99
the available data, and; secondly, linkages between the NACE
2
sectoral codes and the International
Patent Classification (IPC)
3
categories are constructed, leading to the identification of a set of highly
correlated sectors within a region. The merging of these two datasets is enabled by the compatibility of
the IPC V8 and NACE REV.2 codes (van Looy et al, 2014).
Figure 3: Structure of Related Variety Analysis application. Online-S3 Platform (www.s3platform.eu)
Application 3: EDP Focus Groups
Information obtained through the use of Specialisation Indexes and Related Variety Analysis
provide a baseline upon which policy-makers can prioritise wider sectors of the regional economy,
focussing on those sectors that have the greatest potential for growth. However, it is beyond the scope
of these applications to specify precise interventions required to progress.
To meet this requirement, the Entrepreneurial Discovery Process (EDP) Focus Groups
application provides a content management tool that facilitates the organisation of an EDP group
meeting. An additional feature of the application is the provision of publicly-available documents
relating to the process. Such documents might include agendas designed for EDP meetings, as well as
thematic reports related to specific economic sectors which illustrate the main outcomes obtained from
previous EDP activities. This repository of information is a feature of the connected intelligence
approach which underpins the Online-S3 Platform, offering an accumulative expansion of the
knowledge-base, upon which new EDP processes are designed. Sectoral categorisation of the EDP
outcomes is another helpful feature of this application, not only in reducing the search time but in
categorising the results obtained.
Application 4: Intervention Logic
The Intervention Logic application displays the intelligence gathered in the form of a schematic
dashboard that enables users to review and elaborate their selected interventions. The design of an
intervention logic starts with understanding both the problem to be addressed and the desired outcomes
to be achieved, specifying the program logic, and building stakeholder consensus related to this theory
of change. The systematic recording of information, emerging from different phases of the RIS3 design
process, provides an effective way for maximising the impact of the selected strategies. To this end,
this application provides a synthesis of outputs coming from analytics tools, as well as tools for policy
design. The connection between these two different types of information constitutes the main added
value of this application, as it aims to determine the causes of possible implementation failures, as well
as identifying potential solutions based on the evidence-based analysis. A set of questions included in
this application requires the user to rationalise their selections, with regard to the selection of specific
indicators and action plans.
3. Discussion
At present, the platform economy is growing exponentially, affecting transformation processes of
regional economic structures, as well as the emergence of knowledge driven growth models and new
forms of policy design. Sophisticated methods focusing on regional specialisation and related variety
analysis are two core elements of the smart growth paradigm, which itself is grounded in an evidence-
2
NACE codes: Statistical Classification of Economic Activities in the European Community, Rev. 2 (2008).
http://ec.europa.eu/eurostat/ramon/nomenclatures/index.cfm?TargetUrl=LST_NOM_DTL&StrNom=NACE_REV2
3
International Patent Classification (IPC): http://www.wipo.int/classifications/ipc/en/.
100
based approach to policy formation. Within this context platforms can act as facilitators for regional
growth, as they can easily reinforce peer exchange and mutual learning experiences, through their
sharing information character, as well as enable regional authorities to adopt sophisticated techniques
throughout their decision-making processes. Innovation emerges as an outcome of this connected
intelligence approach, that aims to link interdisciplinary actions, from data generation to public
participation.
The Online-S3 Platform has introduced an online space for facilitating the design and
implementation of RIS3 strategies. In doing so, the platform intends to foster innovation and smart
growth policies through data exploitation, systematic guidance and sharing economy aspects, such as
public deliberation and peer experience exchange. Applications presented in this paper focus on
facilitating effective prioritisation of emerging regional sectors, where synergies between regions or
thematic areas could arise, reinforcing smart growth. Thus, it becomes evident that online platforms
facilitate knowledge production, by using a central domain to share and disseminate policy outcomes,
leading to proliferation of novel ideas and knowledge spill-overs between the regions, a process that
lies in the heart of smart growth paradigm.
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