Strengthening Smart Specialisation Strategies (S3) through network analysis: Policy insights from a decade of innovation projects in Aragón

Rodríguez Ochoa, David; Arranz, Nieves; Arroyabe, M. F. Article Strengthening Smart Specialisation Strategies (S3) through network analysis: Policy insights from a decade of innovation projects in Aragón Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Rodríguez Ochoa, David; Arranz, Nieves; Arroyabe, M. F. (2025) : Strengthening Smart Specialisation Strategies (S3) through network analysis: Policy insights from a decade of innovation projects in Aragón, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 13, Iss. 8, pp. 1-41, https://doi.org/10.3390/economies13080218 This Version is available at: https://hdl.handle.net/10419/329498 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Academic Editor: Tsutomu Harada Received: 9 May 2025 Revised: 1 July 2025 Accepted: 18 July 2025 Published: 26 July 2025 Citation: Rodríguez Ochoa, D., Arranz, N., & Fernandez de Arroyabe, M. (2025). Strengthening Smart Specialisation Strategies (S3) Through Network Analysis: Policy Insights from a Decade of Innovation Projects in Aragón. Economies,13(8), 218. https://doi.org/10.3390/economies 13080218 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article Strengthening Smart Specialisation Strategies (S3) Through Network Analysis: Policy Insights from a Decade of Innovation Projects in Aragón David Rodríguez Ochoa 1,2,* , Nieves Arranz 3and Marta Fernandez de Arroyabe 4 1 CIRCE Foundation-Research Centre for Energy Resources and Consumption-Zaragoza, 50018 Zaragoza, Spain 2PhD Programme in Economics and Business, UNED, 28040 Madrid, Spain 3Department of Applied Economics, School of Economics and Business, UNED, 28040 Madrid, Spain; [email protected] 4Essex Business School, University of Essex, Southend-on-Sea SS1 1LW, UK; [email protected] *Correspondence: [email protected] Abstract This paper applies a multi-level social network analysis to examine Aragón’s innovation ecosystem, focusing on a decade of competitive public projects (2014–2023) aligned with the region’s Smart Specialisation Strategy (S3) 2021–2027. By mapping and weighting the participation of regional entities across regional, national, and European calls, the study uncovers how all types of local actors organise themselves around key specialisation areas. Moreover, a comparative benchmark is introduced by analysing more than 33,000 Horizon 2020 and Horizon Europe initiatives without Aragonese partners, revealing how to fill structural gaps and enrich the regional ecosystem through international collaboration. Results show strong funding concentration in four fields—Energy, Health, Agri-Food, and Advanced Technologies—while other historically strategic areas like Hydrogen and Water remain underrepresented. Although leading institutions (UNIZAR, CIRCE, ITA, AITIIP) play central roles in connecting academia and industry, direct collaboration among them is limited, pointing to missed synergies. Expanding previous SNA-based assessments, this study introduces a diagnostic tool to guide policy, proposing targeted actions such as challenge-driven calls, dedicated support programs, and cross-border consortia with top EU partners. Applied to two contrasting specialisation areas, the method offers sectorspecific recommendations, helping policymakers align Aragón’s innovation capabilities with EU priorities and strengthen its position in both established and emerging domains. Keywords: regional innovation; smart specialisation strategy; Aragon; competitive public projects; social network analysis; innovation ecosystem 1. Introduction In recent years, smart specialisation strategies (S3) have become key tools for promoting regional competitiveness, sustainability, and inclusiveness in Europe (European Commission, Joint Research Centre,2021;European Committee of the Regions et al.,2023). Instead of simply funding isolated innovation projects, S3 encourages regions to identify and build on their unique strengths through collaborative governance, knowledge sharing, and ongoing entrepreneurial discovery (Asheim,2019;Gheorghiu et al.,2016). This holistic approach views regional innovation systems as complex, adaptive networks shaped by Economies 2025,13, 218 https://doi.org/10.3390/economies13080218 Economies 2025,13, 218 2 of 41 policy structures, social capital, and place-based strategies that influence how ideas emerge, spread, and generate sustainable advantage (Barbero et al.,2022;Mascarenhas et al.,2021). Despite this conceptual evolution, questions remain about how effectively S3 strategies capture and steer these underlying collaboration dynamics in practice. A growing body of literature highlights the importance of moving beyond funding and governance indicators to better understand the relational patterns within innovation ecosystems (Balland et al., 2019;Tolias,2019;Wibisono,2022). This paper addresses that gap by proposing multilevel social network analysis (SNA) to diagnose the structure and dynamics of Aragón’s innovation ecosystem. We apply this framework to Aragón, a region that illustrates both the strengths and limitations of S3 strategies. Despite rising R&D investment and strong GDP per capita, Aragón faces persistent challenges in technology transfer, fragmented R&D capacity, and weak collaboration among research centres. The S3 Aragón 2021–2027 strategy prioritises digitalisation, sustainability, and service innovation linked to industry, alongside improved governance and monitoring (Aragón Government,2024). However, major gaps remain: nearly 60% of local organisations report no access to public R&D funding, research institutions remain poorly coordinated, and large firms tend to engage universities and technology centres through limited, bilateral collaborations rather than broad partnerships. Moving from an Aragón perspective to that of other European regions, a critical gap in S3 implementation lies in the lack of detailed, empirical insight into how regional innovation systems operate in practice. While existing evaluations often focus on funding outputs, strategic planning documents, or stakeholder consultation processes, they tend to explore collaboration patterns only indirectly or in a fragmented manner. As highlighted by Tolias (2019), there is a pressing need to deepen our understanding of how innovation actors interact, particularly in terms of knowledge flows and structural positioning within networks. This mismatch between S3’s conceptual goals (dynamic, place-based, participatory innovation) and the limited tools available to monitor, diagnose and adapt these strategies effectively creates a fundamental gap in tailoring interventions that respond to the real-world challenges of regional innovation networks. Building on earlier work (Calvo-Gallardo et al.,2021,2022;Rodríguez Ochoa et al., 2023,2025), which applied one-mode network analysis to examine R&D project collaborations within Aragón’s RIS3 (2014–2020) context, the present study expands this scope by adopting a multi-domain, comparative framework based on Aragón’s S3 (2021–2027) key areas. The prior research revealed patterns of institutional centrality and identified bottlenecks in regional knowledge diffusion but was limited to addressing the differences between projects funded at different geographical levels. Here, we broaden the analysis by: (i) mapping and weighting participation in competitive public projects from 2014 to 2023 across regional, national, and European levels, using funding as a proxy for collaboration intensity; (ii) classifying these projects according to Aragón’s S3 priority domains to assess whether actors are concentrated or fragmented across areas; and (iii) benchmarking Aragón’s network structures against Horizon 2020 and Horizon Europe projects that exclude Aragonese partners, identifying external models of connectivity that could help fill local gaps. This approach provides policymakers with an evidence-based view of the region’s collaborative architecture, enabling more targeted and adaptive interventions aligned with S3 goals. The remainder of this study is organized as follows: Section 2analyses the theoretical frameworks of the paper’s contributions. Section 3describes the data collection and SNAbased methodology, specifying how projects are classified and weighted. Section 4presents the findings from our participation, cohesion, and centrality analyses, highlighting their implications for innovation bottlenecks across key specialisation areas. Section 5discusses Economies 2025,13, 218 3 of 41 how these empirical results inform theoretical and policy debates on regional innovation strategies, with special attention to bridging institutional gaps. Finally, Section 6concludes by synthesising the study’s contributions to both scholarly literature and practice, outlining limitations and suggesting avenues for future research. 2. Literature Review and Conceptual Framework 2.1. Innovation Through Knowledge Flows and Interactive Learning: Regional Innovation Systems and Smart Specialisation Strategies Innovation a systemic process, not an individual one. It emerges from dynamic interactions among diverse stakeholders, rather than isolated R&D projects (Edquist,1997; B. A. Lundvall,1992;Nelson & Rosenberg,1993). Rooted in the systems of innovation tradition, this view stresses that innovation—especially in knowledge-intensive settings— requires collaboration, as no single actor holds all the necessary expertise. Both smart specialisation strategies (S3) and regional innovation systems (RIS) follow this logic, treating innovation as the outcome of coordinated activity across institutions, not just businesslevel performance. Knowledge is distributed and socially embedded. It resides not in isolated labs, but in the networks, norms, and routines that shape how information is shared (Asheim & Gertler, 2005;Freeman,1995). Access to this knowledge depends on institutional frameworks and proximity, which enable collaboration to bridge gaps in technical and organisational expertise. RIS theory highlights how geographic and institutional closeness fosters learning, while S3 builds on this by urging regions to map and connect their embedded knowledge assets. Interactive learning lies at the core of innovation. It evolves through formal and informal, trust-based exchanges among businesses, universities, governments, and intermediaries (Jensen et al.,2007;B.-Å. Lundvall & Johnson,1994). This learning is cumulative and context dependent. While face-to-face interaction accelerates tacit knowledge exchange, cognitive and relational proximity can substitute for physical distance (Breschi & Lissoni, 2001). Regions, in this sense, become arenas for dense knowledge interaction—making place a key factor in both RIS and S3 frameworks. Introduced in 1997 (Cooke et al.,1997), the RIS approach conceptualizes regions as networks of businesses, universities, intermediaries, and governance structures that support innovation. A well-functioning RIS fosters collaboration, knowledge exchange, and shared access to skilled labour and support systems (Asheim & Gertler,2005;Pinto et al.,2024).Yet many regions face systemic challenges, causing them to struggle to build cohesive systems. Common barriers include coordination failures (Rossoni et al.,2024), institutional fragmentation, especially in large metropolitan areas where numerous organizations exist but their interactions remain siloed (Tödtling & Trippl,2005), and ‘organizational thinness’ in peripheral regions with scarcity of innovation actors (Isaksen et al.,2022;Isaksen & Trippl,2016). These conditions highlight that a “one size fits all” innovation policy is inadequate and, accordingly, innovation policy must adapt to specific contexts. Smart specialisation emerged as a policy response to these challenges. Building on RIS principles, S3 was introduced in the 2014–2020 EU programming period as a condition for cohesion funding (McCann & Ortega-Argilés,2015). It required regions to create research and innovation strategies for smart specialisation (RIS3) aligned with their unique strengths (Foray,2014,2016;Joint Research Centre (European Commission) et al.,2021). Rather than spreading resources thin, S3 encourages regions to focus investments on priority domains with high innovation potential. Economies 2025,13, 218 4 of 41 The entrepreneurial discovery process (EDP) is a defining feature of S3. It brings together businesses, academia, and government to jointly identify promising niches (Gheorghiu et al.,2016;Marinelli & Perianez,2017). This iterative process echoes RIS principles by grounding strategic choices in local knowledge (Szerb et al.,2020). S3 is also place-based: it recognises that top-down policies often miss local nuances (Demblans et al.,2020). By analysing economic structure and institutional capacity, S3 helps regions concentrate resources, build critical mass, and align investments with real strengths (Suedekum,2025). Collaborative governance, in turn, reduces fragmentation (Kroll,2017). In summary, S3 does not replace RIS; it operationalises it. S3 transforms RIS concepts into policy action, giving regions a practical framework to mobilise assets and address systemic weaknesses (Bevilacqua et al.,2015;Rossoni et al.,2024). It offers a focused, participatory approach to strengthening regional innovation systems through strategy, collaboration, and place-based investment. 2.2. From Theory to Practice: Challenges in S3 Implementation The smart specialisation strategy was conceived as a place-based, bottom-up approach to innovation policy, but putting this into practice has proven challenging (Capello & Kroll, 2016). Early experiences showed that the entrepreneurial discovery process (EDP) must be continuous and iterative (Marinelli & Perianez,2017), yet many regions struggle to sustain this engagement. Translating S3’s dynamic, collaborative principles into real governance often clashes with institutional inertia and practical constraints. As recent literature notes, this gap between theory and implementation continues to limit the strategy’s full potential (Esparza-Masana,2022;Foray,2017;Laranja et al.,2022;Molica et al.,2025;Polido et al., 2019;Reid & Maroulis,2017). A key barrier is weak collaboration among the quadruple helix actors—industry, government, academia, and civil society. These ties often remain fragmented, leading stakeholders to operate in silos that undermine trust and limit meaningful interaction. Limited stakeholder engagement beyond S3’s initial design phase compounds the problem. While sustained participation is essential for effective discovery, many regions rely on narrow or symbolic consultation. Inadequate monitoring mechanisms adds to the challenge. S3, as a policy-learning experiment, needs robust feedback systems to track strategic progress and assess the impact of funded projects. Misalignment between S3 priorities and available funding instruments further disrupts implementation. If R&D programs or investment schemes do not support identified priorities, strategies can stall. Institutional inertia also plays a role—networked governance demands new practices from public agencies, but entrenched bureaucracies often resist these changes. Tackling these obstacles is crucial to making S3 work in practice. Here, social network analysis (SNA) offers valuable insights. It allows policymakers to map how actors interact within a regional innovation system and to identify hidden patterns, gaps, and bottlenecks (Calvo-Gallardo et al.,2022;Fernandez de Arroyabe et al.,2021;Rodríguez Ochoa et al., 2025). For instance, it can reveal isolated institutions or overcentralized “gatekeeper” nodes that hinder collaboration. SNA is not just analytical; it is a diagnostic and policy-oriented tool. By exposing weak links and structural imbalances, it helps design targeted interventions, connecting disconnected actors, strengthening inter-organizational ties, or diversifying the governance base. Rather than replacing the EDP, SNA complements it by tracking the network’s health and inclusiveness over time. It turns the abstract idea of innovation networks into actionable data, supporting more adaptive, evidence-based S3 implementation Economies 2025,13, 218 5 of 41 This approach aligns with recent calls for more sustainable, inclusive, and adaptive S3 (European Committee of the Regions et al.,2023), and contributes to the emerging “S3 2.0” agenda, which stresses challenge-oriented, systemic transformation through improved governance and policy alignment (Foray,2023). Likewise, the OECD (OECD,2023) emphasises stronger coordination and monitoring tools to support regions in industrial transition. In this context, SNA offers a practical method to diagnose structural gaps and support more responsive, evidence-based S3 implementation. In this context, Aragón is well suited to examine these challenges. As a “moderate innovator” in the EU’s 2023 Regional Innovation Scoreboard (Hobza et al.,2023), it shares traits with many other European regions: a strong industrial base, active rural sectors, and a diverse institutional landscape (Aragón Government,2025) These characteristics make it a representative case for studying the barriers and opportunities in S3 implementation. 2.3. The Case of Aragon: Innovation Ecosystem and Strategic Priorities Aragón offers a timely and illustrative case of a regional innovation ecosystem under a modern smart specialisation strategy. According to the Spanish National Statistics Institute, the region’s R&D spending rose to 1.16% of GDP in 2023—its highest level since 2003— surpassing previous peaks in 2008–2010. While this marks significant progress, it remains below the EU average (2.2%) and the 3% target. Employment in R&D also reached a record 13.2%, closely aligned with the national figure of 13.3%, and up from 12.6% in 2010. Aragón combines a solid economic base with favourable demographics. With 1.35 million residents (2.8% of Spain’s population), the region contributes 3.1% to national GDP, with a GDP per capita of EUR 31,051. Its S3 Aragón 2021–2027 strategy focuses on digitalisation, sustainability, and industry-linked services, identifying key domains such as Energy, Advanced Technologies, Agri-Food, and Health and Wellbeing. It adopts a more collaborative governance model aligned with the UN Sustainable Development Goals and introduces improved monitoring to support adaptive policy (Aragón Government,2024). However, implementation challenges persist. The region’s industrial share of gross value added dropped from 22.4% in 2000 to 17.8% in 2019, while services rose from 58.3% to 66%. Many companies struggle to find funding aligned with their technological needs, and collaboration between large companies and R&D centres remains limited. The innovation ecosystem includes many scientific and technical actors, but this diversity has not translated into a coherent, coordinated R&D offering. Fragmentation is evident in the low number of collaborative regional projects and the absence of tools to effectively connect companies, researchers, and technology providers. Nearly 60% of organisations report not receiving public R&D support, especially in rural areas, revealing weak outreach and gaps in technology transfer. These issues call for more targeted, coordinated interventions to unlock the region’s innovation potential. Aragón’s context reflects the broader landscape of many mid-sized European regions. As a “Moderate Innovator” in the EU Regional Innovation Scoreboard (Hobza et al.,2023), it blends a strong industrial base with extensive rural areas and an emerging R&D system. At the same time, it brings unique assets. It leads in renewable energy, which now accounts for over 80% of electricity generation (Red Eléctrica,2024), and its rising R&D investment signals growing political and institutional commitment. The region also engages actively in the S3 Community of Practice and related EU networks, using peer learning to refine its approach (European Commission,2025). This combination of shared challenges and distinctive momentum makes Aragón a valuable case study. Its experience offers relevant lessons for designing and implementing smart specialisation strategies in comparable regions across Europe. Economies 2025,13, 218 6 of 41 2.4. Research Questions Recent literature on RIS3 and S3 has provided a range of qualitative and quantitative insights into the dynamics of innovation at a regional level (Balland et al.,2019;CalvoGallardo et al.,2022). However, several critical gaps remain. First, few studies provide multi-level, long-term benchmarks of competitive public project networks. Most focus on static snapshots rather than the evolution of innovation ecosystems across regional, national, and European scales. Second, although SNA is gaining traction, it is rarely applied in RIS contexts with an eye toward feedback loops for policy adaptation. Finally, few analyses link empirical network metrics to specific policy interventions, leaving a disconnect between theory and actionable strategies. This study addresses these gaps by applying SNA to public R&D project networks in Aragón. Focusing on projects funded through regional, national, and European calls from 2014 to 2023, and classified by S3 2021–2027 specialisation areas, the research generates multi-level benchmarks and actionable insights to inform regional policy. The following research questions guide the analysis: • RQ1: How do the features of competitive public project networks from 2014 to 2023 within the S3 2021–2027 specialisation areas reveal the structure and dynamics of Aragón’s innovation ecosystem? This question maps Aragón’s innovation network over a decade, addressing the first research gap. By analysing participation, cohesion and centrality metrics, the study reveals how stakeholders interact—highlighting well-connected clusters, isolated nodes, and structural vulnerabilities. These findings offer a systemic view of local strengths and weaknesses and assess whether current strategies effectively mobilise the region’s capacities. • RQ2: What roles do key entities play in knowledge transfer within these specialisation areas, and how do their centrality positions influence the dynamism of Aragón’s regional innovation ecosystem? While prior work often neglects the influence of individual actors, this question examines how central entities shape innovation dynamics. Using degree, betweenness, closeness, and eigenvector centrality measures, we identify which actors act as bridges and knowledge hubs. These insights link structural network data to knowledge flows and support more targeted, evidence-based policy interventions. • RQ3: How can the analysis of the last decade innovation networks involving nonAragonese partners enhance Aragón’s innovation policies and strengthen its position in a targeted specialisation area? A novel aspect of this work, compared to our earlier study (Rodríguez Ochoa et al., 2025), is the detailed analysis of participation metrics by both specialisation area and type of entity for projects funded under H2020 and Horizon Europe (EU Framework Program calls) during 2014–2023, specifically excluding Aragonese partners. These projects account for 78% of the region’s total innovation funding. By benchmarking Aragón’s networks against European examples, we highlight opportunities to enhance local practices through transnational collaboration. This analysis explores how cross-border partnerships contribute to innovation and offers clear, transferable lessons to improve regional policy and strategic alignment. Economies 2025,13, 218 7 of 41 3. Materials and Methods 3.1. Data The present study examines the innovation networks established in the Aragón region over the past decade, categorising them according to different specialisation areas. To achieve this, our analysis builds upon the dataset presented in our previous work (Rodríguez Ochoa et al.,2025), which encompasses all competitive public projects at regional, national, and European levels that involved at least one partner from the Aragón region during the period 2014–2023. In this study, we further refined the dataset by labelling each project according to the specialisation areas defined in the Aragón Smart Specialisation Strategy 2021–2027 (Aragón Government,2024). The strategy document was also examined in detail to incorporate its identified bottlenecks into our analysis. Additionally, to establish a benchmark for the Aragonese innovation ecosystem, our current research extends the previous approach by incorporating data from all projects funded under the Horizon 2020 and Horizon Europe programs (Framework Program projects) during the same period (2014–2023), excluding those projects that involved Aragonese partners. This comparative analysis facilitates a broader understanding of regional performance relative to the European context. 3.1.1. Data Sources, Collection and Preparation All funding programs examined in this study are based on competitive public calls, in which multiple independent entities submit proposals to secure financing, with selection decisions made according to the merits of their projects and alignment with predefined objectives. This competitive process is distinct from nominative funding, where projects are directly financed without a competitive evaluation—often earmarked for specific entities or initiatives without the need for proposal submission and review. Based on this framework, the primary data sources for this study are shown in Figure 1. Figure 1. Project data sources considered in the analysis. All the calls considered in the study are specified in our previous research study (Rodríguez Ochoa et al.,2025). Moreover, Appendix Aincludes additional information on the different databases consulted and the reasoning behind choosing these datasets. To ensure the accuracy and relevance of the data to Aragón’s innovation ecosystem, several rigorous processing steps were taken, as shown in Figure 2: Economies 2025,13, 218 8 of 41 Figure 2. Data treatment process. These systematic steps ensured that the data prepared for analysis is both precise and relevant, thereby allowing our evaluation of Aragón’s innovation ecosystem and the provision of policy recommendations to address the bottlenecks identified in the S3 2021–2027 and improve the positioning of the region at European level in targeted specialisation areas. 3.1.2. Entities and Projects Attributes In order to classify the participating entities based on their inherent characteristics and primary activities, we have defined six distinct categories: • Public Sector (PUB): This category comprises governmental authorities at the national, regional, and local levels, along with energy agencies. • Higher Education Institutions (HES): This category mainly consists of universities that are actively engaged in both teaching and research. • Research Organizations (REC): This group includes two main types: publicly funded national research centres and predominantly private, non-profit research and technology organizations. •Private Companies: This category is further divided into: # Large Private Companies (PRC): Enterprises that exceed the criteria for small and medium-sized companies. # Micro, Small, and Medium-sized Enterprises (PRC-SME): Businesses classified as micro, small, or medium according to the standards set by EU Recommendation 2003/361 (European Commission,2003). • Others (OTH): This group encompasses sector-specific associations and may also include certain research institutes organised as associations. With respect to the roles of entities within projects, each consortium is managed by a coordinator, while the other members are considered participants. This classification does not distinguish between full partners, who sign the grant agreement, and third parties that have a legal or financial link to a beneficiary, as the number of such third parties is negligible and will not be analysed separately in this study. In a novel extension to our previous research, each entity participation within the network has been assigned to one of the specialisation areas appearing in Figure 3(as stated in the Aragón S3 2021–2027) to delineate the thematic focus of the entities involved: Economies 2025,13, 218 15 of 41 Table 6. Collaborations between the non-Aragonese entities of the ecosystem and the key entities. #Entity Country Entity Type Projects in the Ecosystem Collaborations with the Ecosystem’s Key Entities UNIZAR CIRCE ITA AITIIP 1AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICAS ES REC 89 25 5 3 1 2FRAUNHOFER GESELLSCHAFT ZUR FORDERUNG DER ANGEWANDTEN FORSCHUNG EV DE REC 79 14 4 11 4 3ETHNIKO KENTRO EREVNAS KAI TECHNOLOGIKIS ANAPTYXIS (CERTH) EL REC 58 4 22 2 0 4 FUNDACION TECNALIA RESEARCH & INNOVATION ES REC 56 6 5 3 0 5CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS FR REC 52 19 0 5 2 6 CONSIGLIO NAZIONALE DELLE RICERCHE IT REC 45 15 1 1 1 7 UNIVERSITAT POLITECNICA DE VALENCIA ES HES 43 5 1 2 0 8COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES FR REC 42 9 6 4 2 9 RINA CONSULTING SPA IT PRC 40 0 38 1 1 10 UNIVERSIDAD POLITECNICA DE MADRID ES HES 38 5 0 2 1 11 TEKNOLOGIAN TUTKIMUSKESKUS VTT OY FI REC 31 7 4 2 1 12 DANMARKS TEKNISKE UNIVERSITET DK HES 30 11 1 2 0 13 TECHNISCHE UNIVERSITEIT DELFT NL HES 30 13 3 3 2 14 ALMA MATER STUDIORUM—UNIVERSITA DI BOLOGNA IT HES 29 9 0 1 0 15 FUNDACION CARTIF ES REC 28 0 9 3 0 16 NEDERLANDSE ORGANISATIE VOOR TOEGEPAST NATUURWETENSCHAPPELIJK ONDERZOEK TNO NL REC 28 7 2 2 1 17 FUNDACIO EURECAT ES REC 26 2 0 2 0 18 RISE RESEARCH INSTITUTES OF SWEDEN AB SE REC 26 4 1 7 2 19 ASOCIACION DE INVESTIGACION METALURGICA DEL NOROESTE ES REC 23 0 1 8 4 20 INSTITUT NATIONAL DE LA SANTE ET DE LA RECHERCHE MEDICALE FR REC 23 6 0 1 0 Totals 816 161 103 65 22 Economies 2025,13, 218 16 of 41 As Table 5shows, the key players within Aragón’s innovation ecosystem engage in limited collaboration among themselves. However, among the few joint initiatives that do exist, private research centres have the highest number of projects in common with the four key entities, with CIRCE involved in 11 shared projects and AITIIP in eight. Notably, five of these projects involve direct collaboration between CIRCE and AITIIP. In relative terms, nearly one-fourth of the projects in which AITIIP participates are carried out in partnership with the other three key entities, highlighting this organisation’s greater dependence on these ties for its research activities. Conversely, public innovation institutions appear less inclined to collaborate with the other key players. A possible explanation for this trend is the existence of specific regional funding programs that promote direct partnerships between public research organizations and private companies. These targeted calls may reduce the need for public entities to engage in knowledge exchange with other R&D agents, as they already have dedicated mechanisms for fostering innovation through industry collaborations. On the other hand, Table 6highlights that the principal non-Aragonese participants in the ecosystem are predominantly European research centres and universities. Notably, seven of these entities are based in Spain, with the Agencia Estatal Consejo Superior de Investigaciones Científicas (CSIC) emerging as the most integrated external partner, participating in 89 projects. Following CSIC, Fundación Tecnalia Research & Innovation and Universitat Politecnica de Valencia have significant involvement, engaging in 56 and 43 projects, respectively. Beyond Spanish institutions, several prominent research centres stand out for their collaborations with Aragonese entities. Fraunhofer from Germany has participated in 79 projects, primarily collaborating with UNIZAR and ITA. The Greek Research Centre CERTH has engaged in 58 projects, mainly in partnership with CIRCE. Additionally, the French National Centre for Scientific Research (CNRS) and the Italian National Research Council (CNR) have been frequent collaborators with UNIZAR, participating in 52 and 45 projects, respectively. The French Alternative Energies and Atomic Energy Commission (CEA) has also been notably active, collaborating mainly with UNIZAR and CIRCE across 42 projects. Among the top 20 collaborators, RINA Consulting S.p.A. from Italy is the sole corporate entity, participating in 40 projects, with a notable 38 collaborations involving CIRCE. Within the Aragonese institutions, UNIZAR distinguishes itself by engaging in 161 collaborative projects with major non-Aragonese participants. CIRCE follows with 103 such collaborations. ITA, while less involved than UNIZAR and CIRCE, has still participated in 65 projects with all of the top 20 non-Aragonese entities. In contrast, AITIIP focuses on more specialised partnerships within its area of expertise, accounting for 22 collaborations with these leading external participants. 4.1.2. Analysis by Entity Type Tables 7and 8illustrate the distribution of funding and participations among different types of entities within the Aragonese innovation ecosystem, while Tables 9and 10 provide a comparative benchmark from the European Union Framework Program projects (Horizon 2020 and Horizon Europe) spanning the period 2014–2023, excluding those projects with Aragonese entities. The comparison offers critical insights into the alignment of the Aragonese ecosystem with broader European trends, highlighting structural strengths and potential gaps in collaboration patterns. The distribution of funding across entity types in the Aragonese ecosystem (Table 7) reveals distinct patterns based on specialisation areas. Research organizations (REC) emerge as the most prominent funding recipients across nearly all domains, receiving on average 27.67% of total funding. This trend aligns with their role as key enablers of innovation, Economies 2025,13, 218 17 of 41 particularly in research-intensive areas such as Basic Science (33.30%), Biodiversity (27.97%), and Climate Change (33.84%). Higher education institutions (HES) also secure substantial funding (20.17% overall), with their strongest presence in Basic Science (43.39%), Biodiversity (48.81%), and Artificial Intelligence (29.01%). Table 7. Funding distribution by entity type in the Aragonese ecosystem. Specialisation Area HES OTH PRC PRC-SME PUB REC Advanced Technologies (ICT and Industry) 17.77% 5.09% 29.87% 21.59% 0.74% 24.94% Aerospace 4.69% 1.58% 55.83% 12.80% 0.13% 24.98% Agri-Food and Circular Economy 20.22% 7.93% 14.93% 25.89% 2.43% 28.59% Artificial Intelligence 29.01% 3.88% 16.83% 22.42% 1.09% 26.77% Basic Science 43.39% 3.38% 13.03% 6.81% 0.09% 33.30% Biodiversity 48.81% 2.00% 8.20% 12.72% 0.30% 27.97% Climate Change 20.36% 7.06% 15.06% 16.13% 7.55% 33.84% Cultural and Creative Industries and Sectors 16.76% 17.14% 20.34% 12.17% 18.81% 14.79% Energy and Green Fuels 12.79% 6.09% 30.71% 21.75% 2.79% 25.88% Governance and Business Development 26.45% 13.17% 10.88% 13.48% 6.57% 29.44% Health and Wellbeing 24.28% 6.67% 7.06% 9.35% 19.50% 33.14% Hydrogen 5.81% 3.24% 47.39% 21.47% 3.13% 18.96% Security and Defence 13.24% 4.04% 26.84% 21.26% 11.50% 23.12% Sustainable Mobility 10.69% 8.62% 29.72% 19.32% 10.75% 20.89% Water 14.60% 7.70% 11.32% 12.51% 24.77% 29.10% Total average 20.17% 6.46% 21.42% 17.36% 6.93% 27.67% Table 8. Participation distribution by entity type in the Aragonese ecosystem. Specialisation Area HES OTH PRC PRC-SME PUB REC Advanced Technologies (ICT and Industry) 15.59% 12.48% 18.26% 32.16% 1.72% 19.80% Aerospace 9.92% 9.16% 22.14% 20.61% 2.29% 35.88% Agri-Food and Circular Economy 16.54% 16.83% 11.89% 31.42% 4.10% 19.20% Artificial Intelligence 22.10% 9.57% 15.03% 32.35% 2.28% 18.68% Basic Science 39.90% 2.53% 19.61% 13.15% 2.01% 22.81% Biodiversity 25.37% 19.40% 8.96% 13.43% 10.45% 22.39% Climate Change 16.07% 15.00% 12.50% 18.93% 12.86% 24.64% Cultural and Creative Industries and Sectors 14.78% 27.00% 5.58% 11.76% 30.17% 10.71% Energy and Green Fuels 12.88% 10.48% 26.91% 24.79% 4.71% 20.22% Governance and Business Development 22.69% 19.17% 7.87% 15.00% 13.61% 21.67% Health and Wellbeing 28.57% 10.25% 8.93% 15.56% 12.50% 24.19% Hydrogen 10.18% 9.30% 27.02% 23.33% 4.74% 25.44% Security and Defence 14.29% 9.77% 19.17% 26.32% 11.28% 19.17% Sustainable Mobility 10.22% 14.00% 27.44% 20.17% 11.88% 16.30% Water 12.40% 20.12% 9.96% 20.93% 14.23% 22.36% Total average 18.68% 12.87% 17.09% 23.53% 7.28% 20.55% In contrast, private companies (PRC) and SMEs (PRC-SME) exhibit higher participation in technology-driven sectors, with notable funding shares in Aerospace (55.83% PRC, 12.80% PRC-SME), Hydrogen (47.39% PRC, 21.47% PRC-SME), and Sustainable Mobility (29.72% PRC, 19.32% PRC-SME). These figures suggest a concentration of industrial innovation in high-tech and applied research fields, particularly those with strong market applications. Economies 2025,13, 218 18 of 41 Public sector entities (PUB) display a more limited role, accounting for only 6.93% of total funding. Their involvement is slightly higher in governance-related fields, such as Water (24.77%), Cultural and Creative Industries (18.81%), and Health and Wellbeing (19.50%), likely reflecting public policy priorities and regulatory-driven research agendas. Examining participation patterns (Table 8) reinforces these observations, demonstrating the degree to which different entity types engage in collaborative project networks. The research and higher education sectors again dominate, with HES (18.68%) and REC (20.55%) showing strong engagement across all domains. Notably, HES participation peaks in Basic Science (39.90%), Artificial Intelligence (22.10%), and Health and Wellbeing (28.57%), suggesting these areas benefit from strong academic involvement. Private sector engagement follows a distinct pattern, with PRC (17.09%) and PRCSME (23.53%) demonstrating a broader footprint in Advanced Technologies (ICT and Industry) (18.26% PRC, 32.16% PRC-SME), Hydrogen (27.02% PRC, 23.33% PRC-SME), and Security and Defence (19.17% PRC, 26.32% PRC-SME). These figures highlight the relevance of SMEs in the Aragonese ecosystem, positioning themselves as the entity type with most participation, even if, regarding funding, large companies and research centres and universities allocate greater amounts. The comparison with the EU Framework Program (Tables 9and 10) provides further insight. In the broader European landscape, HES and REC exhibit stronger dominance, both in participation (50.84% HES and 27.93% REC) and funding allocation (51.93% HES and 31.70% REC). This highlights a greater reliance on academic and research institutions at the European level, where private sector participation in high-risk research remains lower. Conversely, PRC entities receive relatively higher funding in the Aragonese ecosystem (21.42% PRC versus 8.86% PRC in EU projects), indicating that industry-led innovation plays a more prominent role at the regional level. Table 9. Funding distribution by entity type in the EU Framework Program projects. Specialisation Area HES OTH PRC PRC-SME PUB REC Advanced Technologies (ICT and Industry) 38.74% 2.20% 15.91% 6.53% 0.18% 36.44% Aerospace 37.42% 0.64% 24.28% 1.09% 0.10% 36.47% Agri-Food and Circular Economy 44.65% 5.15% 4.24% 7.62% 2.33% 36.02% Artificial Intelligence 52.86% 1.67% 9.52% 5.16% 0.31% 30.48% Basic Science 65.19% 0.58% 2.39% 1.09% 0.13% 30.62% Biodiversity 64.23% 0.90% 0.41% 0.44% 3.57% 30.46% Climate Change 53.37% 1.78% 3.94% 1.73% 2.36% 36.82% Cultural and Creative Industries and Sectors 80.32% 0.64% 1.94% 1.83% 0.73% 14.55% Energy and Green Fuels 32.78% 3.33% 12.76% 7.24% 2.11% 41.78% Governance and Business Development 47.88% 8.77% 11.51% 5.06% 3.11% 23.67% Health and Wellbeing 63.61% 0.99% 3.06% 2.18% 3.12% 27.04% Hydrogen 24.99% 1.64% 25.82% 15.59% 0.86% 31.09% Security and Defence 46.47% 1.09% 13.50% 7.30% 1.17% 30.49% Sustainable Mobility 31.14% 5.67% 24.33% 5.82% 4.34% 28.70% Water 45.59% 3.01% 3.10% 4.12% 3.70% 40.48% Total average 51.93% 2.37% 8.06% 4.07% 1.88% 31.70% Another takeaway from this analysis is the higher funding allocation of the Aragonese public sector and intermediary organizations (OTH) compared to EU benchmarks. Public institutions at the EU level secure an average of 2.53% of funding, compared to 6.93% in Aragón, suggesting a stronger regulatory and governance role at the regional level. Similarly, intermediary organisations (OTH) exhibit three times more involvement regionally Economies 2025,13, 218 19 of 41 (6.46%) than at the European scale (2.37%). The significant number of sectoral clusters in the Aragón region, coupled with their active participation in specific national funding calls essential for financial sustainability, may account for these figures. For instance, in 2023, the Ministry of Industry and Tourism allocated EUR 10.6 million to support 59 projects from Aragonese business associations through the Program for Supporting Innovative Business Clusters. This made Aragón the second-highest recipient of such funds among Spanish regions (El Periódico,2023). Table 10. Participation distribution by entity type in the EU Framework Program projects. Specialisation Area HES OTH PRC PRC-SME PUB REC Advanced Technologies (ICT and Industry) 40.06% 2.21% 18.64% 10.18% 0.54% 28.37% Aerospace 49.70% 1.30% 10.52% 2.78% 0.56% 35.14% Agri-Food and Circular Economy 42.09% 7.93% 5.85% 9.63% 3.20% 31.30% Artificial Intelligence 49.17% 1.77% 11.81% 7.49% 0.79% 28.97% Basic Science 63.79% 0.86% 4.14% 2.35% 0.31% 28.55% Biodiversity 61.80% 1.67% 0.60% 0.89% 4.23% 30.81% Climate Change 53.82% 2.21% 2.49% 2.56% 3.31% 35.61% Cultural and Creative Industries and Sectors 75.51% 1.31% 2.58% 2.78% 2.14% 15.67% Energy and Green Fuels 35.94% 5.61% 14.80% 10.77% 2.86% 30.02% Governance and Business Development 50.35% 6.57% 6.64% 6.88% 3.89% 25.66% Health and Wellbeing 62.11% 1.72% 4.65% 3.64% 3.17% 24.70% Hydrogen 29.06% 4.44% 19.81% 13.47% 1.68% 31.54% Security and Defence 43.41% 1.67% 14.75% 9.45% 2.56% 28.16% Sustainable Mobility 32.64% 7.47% 22.40% 7.95% 4.99% 24.55% Water 44.29% 4.93% 4.61% 5.23% 4.41% 36.53% Total average 50.84% 3.51% 8.86% 6.33% 2.53% 27.93% 4.1.3. Advanced Technologies Analysis Tables 11–13 show the top 10 entities with the highest amounts of funding achieved, participation in projects and coordinated initiatives, respectively in the Aragonese ecosystem and the specialisation area of Advanced Technologies, while Tables 14–16 show the same information for the benchmark from the European Union Framework Program. Table 11. Top 10 entities by funding achievement in Advanced Technologies within the Aragonese ecosystem. # Entity Name Type Country Funding Achieved (EUR) 1 INSTITUTO TECNOLOGICO DE ARAGON REC ES 22,335,621 2 UNIVERSIDAD DE ZARAGOZA HES ES 19,668,424 3 BSH ELECTRODOMESTICOS ESPANA SA PRC ES 18,935,696 4 INTERUNIVERSITAIR MICRO-ELECTRONICA CENTRUM REC BE 12,591,788 5 FUNDACION AITIIP REC ES 10,533,492 6FRAUNHOFER GESELLSCHAFT ZUR FORDERUNG DER ANGEWANDTEN FORSCHUNG EV REC DE 9,930,329 7 INFINEON TECHNOLOGIES AG PRC DE 7,520,076 8FUNDACION CIRCE CENTRO DE INVESTIGACION DE RECURSOS Y CONSUMOS ENERGETICOS REC ES 7,335,004 9 TELTRONIC, S.A. PRC ES 6,743,792 10 SOCIEDAD ANONIMA INDUSTRIAS CELULOSA ARAGONESA PRC ES 6,691,261 Economies 2025,13, 218 20 of 41 ITA emerges as the leading entity in Aragón in terms of funding secured, receiving EUR 22.3M, followed closely by the UNIZAR (EUR 19.6M) and BSH Electrodomésticos España (EUR 18.9M). These figures highlight the strong presence of both research institutions (REC) and higher education establishments (HES) in attracting project financing, with the private sector also playing a prominent role. Other notable recipients include AITIIP (EUR 10.5M) and CIRCE (EUR 7.3M), reinforcing the strategic position of private research centres in securing competitive project funding. Table 12. Top 10 entitiesby numberof projects in Advanced Technologies withinthe Aragonese ecosystem. # Entity Name Type Country Number of Projects 1 INSTITUTO TECNOLOGICO DE ARAGON REC ES 154 2 UNIVERSIDAD DE ZARAGOZA HES ES 106 3 FUNDACION AITIIP REC ES 36 4 ASOCIACION CLUSTER DE AUTOMOCION DE ARAGON OTH ES 25 5ASOCIACION ESPANOLA DE FABRICANTES EXPORTADORES DE MAQUINARIA PARA CONSTRUCCION, OBRAS PUBLICAS Y MINERIA OTH ES 25 6 ASOCIACION LOGISTICA INNOVADORA DE ARAGON OTH ES 22 7AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICAS REC ES 21 8FUNDACION CIRCE CENTRO DE INVESTIGACION DE RECURSOS Y CONSUMOS ENERGETICOS REC ES 18 9 BSH ELECTRODOMESTICOS ESPANA SA PRC ES 17 10 CLUSTER DE EMPRESAS DE TECNOLOGÍAS DE LA INFORMACIÓN, ELECTRÓNICA Y TELECOMUNICACIONES DE ARAGÓN OTH ES 17 In terms of the number of participations, ITA again leads with 154 projects, followed by UNIZAR (106 projects). AITIIP is the most active private technology centre, with 36 project participations, while clusters and sector associations such as CAAR (automotive cluster, 25 projects) and ALIA (logistics cluster, 22 projects) demonstrate the role of intermediary organizations (OTH) in fostering industrial collaboration. Table 13. Top 10 entities by number of coordination actions in Advanced Technologies within the Aragonese ecosystem. # Entity Name Type Country Coordination Actions 1ASOCIACION ESPANOLA DE FABRICANTES EXPORTADORES DE MAQUINARIA PARA CONSTRUCCION, OBRAS PUBLICAS Y MINERIA OTH ES 23 2 ASOCIACION CLUSTER DE AUTOMOCION DE ARAGON OTH ES 22 3 ASOCIACION LOGISTICA INNOVADORA DE ARAGON OTH ES 14 4 BSH ELECTRODOMESTICOS ESPANA SA PRC ES 13 5CLUSTER DE EMPRESAS DE TECNOLOGÍAS DE LA INFORMACIÓN, ELECTRÓNICA Y TELECOMUNICACIONES DE ARAGÓN OTH ES 12 6 Cluster de la Salud de Aragón (ARAHEALTH) OTH ES 11 7ASOCIACION CLUSTER PARA EL USO EFICIENTE DEL AGUA-ZINNAE OTH ES 10 8 SOCIEDAD ANONIMA INDUSTRIAS CELULOSA ARAGONESA PRC ES 10 9 CLUSTER ESPAÑOL DE PRODUCTORES DE GANADO PORCINO OTH ES 9 10 INSTITUTO TECNOLOGICO DE ARAGON REC ES 8 Economies 2025,13, 218 21 of 41 Coordination actions provide a measure of leadership within collaborative projects. As shown in Table 13, OTH organisations dominate coordination efforts, with sector clusters taking the top three positions, due to their role as promoters in Spanish national calls for innovative companies’ clusters. The presence large companies among the top coordinators highlights how industry-led initiatives play an integral role in project leadership. Interestingly, ITA (eight coordination actions) ranks lower in coordination than in funding and participation, suggesting a preference for participation over project leadership. Table 14. Top 10 entities by funding achievement in Advanced Technologies within the EU Framework Program projects. # Entity Name Type Country Funding Achieved (EUR) 1FRAUNHOFER GESELLSCHAFT ZUR FORDERUNG DER ANGEWANDTEN FORSCHUNG EV REC DE 147,192,091 2 INTERUNIVERSITAIR MICRO-ELECTRONICA CENTRUM REC BE 116,664,074 3COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES REC FR 110,988,667 4 CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS REC FR 58,115,888 5 TEKNOLOGIAN TUTKIMUSKESKUS VTT OY REC FI 53,547,020 6 CHALMERS TEKNISKA HOGSKOLA AB HES SE 53,044,483 7 TECHNISCHE UNIVERSITEIT EINDHOVEN HES NL 36,136,424 8BARCELONA SUPERCOMPUTING CENTER CENTRO NACIONAL DE SUPERCOMPUTACION REC ES 35,193,681 9 THE UNIVERSITY OF MANCHESTER HES UK 31,789,091 10 FUNDACION TECNALIA RESEARCH & INNOVATION REC ES 29,740,228 As shown in Table 14, at the European level, the dominance of major research and technology institutions is evident. Fraunhofer (EUR 147.1M, Germany), Interuniversity Microelectronics Centre (IMEC, EUR 116.6M, Belgium), and CEA (EUR 110.9M, France) lead in total funding received. Notably, Spanish representation is seen in Barcelona Supercomputing Center (EUR 35.1M) and Tecnalia (EUR 29.7M), highlighting Spain’s competitive position in supercomputing and applied research. Table 15. Top 10 entities by number of projects in Advanced Technologies within the EU Framework Program projects. # Entity Name Type Country Number of Projects 1FRAUNHOFER GESELLSCHAFT ZUR FORDERUNG DER ANGEWANDTEN FORSCHUNG EV REC DE 199 2COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES REC FR 113 3 CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS REC FR 113 4 INTERUNIVERSITAIR MICRO-ELECTRONICA CENTRUM REC BE 95 5 TEKNOLOGIAN TUTKIMUSKESKUS VTT OY REC FI 69 6 CHALMERS TEKNISKA HOGSKOLA AB HES SE 63 7 POLITECNICO DI MILANO HES IT 54 8 TECHNISCHE UNIVERSITEIT EINDHOVEN HES NL 53 9 IDRYMA TECHNOLOGIAS KAI EREVNAS REC EL 52 10 CONSIGLIO NAZIONALE DELLE RICERCHE REC IT 52 Economies 2025,13, 218 22 of 41 When compared to the top Aragonese recipients, it is evident that regional actors operate on a significantly smaller financial scale. ITA, Aragón’s highest-funded entity (EUR 22.3M), would rank outside the top 10 at the European level, demonstrating the funding gap between regional and EU-wide research networks. Table 15 shows that Fraunhofer again leads in project participation (199 projects), followed by CEA (113 projects) and CNRS (113 projects). IMEC (95 projects) and Teknologian Tutkimuskeskus VTT (69 projects, Finland) further exemplify the role of major European research institutes in shaping technological advancement. By comparison, the highest-ranked Aragonese entity, ITA (154 projects), would be third on the European list, demonstrating strong regional engagement despite funding disparities. UNIZAR (106 projects) also shows significant activity, reinforcing its role in Aragón’s innovation landscape. Finally, Table 16 shows that European coordination efforts are also led by research organisations, with Fraunhofer (23 actions), CNRS (22 actions), and CEA (21 actions) demonstrating their leadership. Table 16. Top 10 entities by number of coordination actions in Advanced Technologies within the EU Framework Program projects. # Entity Name Type Country Coordination Actions 1FRAUNHOFER GESELLSCHAFT ZUR FORDERUNG DER ANGEWANDTEN FORSCHUNG EV REC DE 23 2 CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS REC FR 22 3COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES REC FR 21 4EREVNITIKO PANEPISTIMIAKO INSTITOUTO SYSTIMATON EPIKOINONION KAI YPOLOGISTON REC EL 18 5 CHALMERS TEKNISKA HOGSKOLA AB HES SE 18 6 INTERUNIVERSITAIR MICRO-ELECTRONICA CENTRUM REC BE 16 7 TEKNOLOGIAN TUTKIMUSKESKUS VTT OY REC FI 14 8 UNIVERSITEIT GENT HES BE 12 9 TECHNISCHE UNIVERSITEIT EINDHOVEN HES NL 12 10 DANMARKS TEKNISKE UNIVERSITET HES DK 12 To summarise, a notable contrast emerges when comparing Aragon’s innovation ecosystem to the broader European Union framework. In Aragón, clusters and industry organizations predominantly lead coordination efforts, whereas at the EU level, public research institutions primarily assume project leadership roles. 4.1.4. Security and Defence Analysis In contrast to the well-established Advanced Technologies sector, the Security and Defence domain in Aragón is currently in an emergent phase. Following the same format as the previous analysis, Tables 17–19 present the top 10 entities in Aragón in this specialisation area, ranked by funding achieved, number of projects, and coordination actions. The highest-funded entity in Aragon’s Security and Defence sector is INSTALAZA, a local private company (PRC) specializing in defence technologies, with a total funding of EUR 6.3 million. This stresses the role of industry-led innovation in this sector, a trend further reinforced by Equipos Móviles de Campaña ARPA (ARPA) (PRC-SME, EUR 2.45 million), another local specialized company ranked fourth after Fraunhofer (REC, EUR 4.6 million, Germany) and the French public entity Département des Hautes-Pyrénées Economies 2025,13, 218 23 of 41 (PUB, EUR 3.02 million, France). Among the main Aragonese key players, only UNIZAR is present, occupying the seventh position with EUR 2.14 million. Table 17. Top 10 entities by funding achievement in Security and Defence within the Aragonese ecosystem. # Entity Name Type Country Funding Achieved (EUR) 1 INSTALAZA, S.A. PRC ES 6,310,358 2FRAUNHOFER GESELLSCHAFT ZUR FORDERUNG DER ANGEWANDTEN FORSCHUNG EV REC DE 4,607,408 3 Département des Hautes-Pyrénées PUB FR 3,020,339 4EQUIPOS MOVILES DE CAMPAÑA ARPA SA PRCSME ES 2,455,290 5 UNIVERZA V LJUBLJANI HES SI 2,215,374 6 STIFTELSEN NORGES GEOTEKNISKE INSTITUTT REC NO 2,173,000 7 UNIVERSIDAD DE ZARAGOZA HES ES 2,141,064 8 TOTALFORSVARETS FORSKNINGSINSTITUT REC SE 2,074,721 9TDW GESELLSCHAFT FUR VERTEIDIGUNGSTECHNISCHE WIRKSYSTEME MIT BESCHRANKTE RHAFTUNG PRC DE 2,000,842 10 COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES REC FR 1,991,516 Table 18. Top 10 entities by number of projects in Security and Defence within the Aragonese ecosystem. # Entity Name Type Country Number of Projects 1 INSTALAZA, S.A. PRC ES 8 2 UNIVERSIDAD DE ZARAGOZA HES ES 6 3 INSTITUTO TECNOLOGICO DE ARAGON REC ES 5 4FRAUNHOFER GESELLSCHAFT ZUR FORDERUNG DER ANGEWANDTEN FORSCHUNG EV REC DE 4 5FUNDACION PARA EL DESARROLLO DE LAS NUEVAS TECNOLOGIAS DEL HIDROGENO EN ARAGON REC ES 3 6 Agrupación Europea de Cooperación Territorial Espacio Portalet OTH ES 2 7ASOCIACION AGRARIA DE JOVENES AGRICULTORES ALTO ARAGON OTH ES 2 8ASOCIACION GENERAL DE PRODUCTORES DE MAIZ EN ESPAÑA—INSTITUTO AGRICOLA Y GANADERO OTH ES 2 9 ASOCIACION NACIONAL DE MAQUINARIA AGROPECUARIA, FORESTAL Y DE ESPACIOS VERDES OTH ES 2 10 CENTRO DE INVESTIGACION ECOLOGICA Y APLICACIONES FORESTALES REC ES 2 INSTALAZA also leads in project participation with eight projects, confirming its dominance in the regional ecosystem. UNIZAR (six projects) and ITA (five projects) further emphasize the role of academic and applied research centres. Interestingly, multiple sectoral organizations (OTH) participate in two projects each, including the Agrupación Europea de Cooperación Territorial Espacio Portalet, ASAJA Alto Aragón, and Asociación Nacional de Maquinaria Agropecuaria, Forestal y de Espacios Verdes. This suggests a diverse range of actors contributing to security-related innovation, including those focused on agriculture, logistics, and environmental risk management. INSTALAZA also emerges as the leading coordinator, heading six projects, reinforcing its strategic role in leading regional security initiatives (Table 17). Notably, various SMEs Economies 2025,13, 218 24 of 41 and specialised technology firms also take on leadership roles but with only a low number of projects. Unlike Advanced Technologies specialisation area, where research institutions played a stronger coordination role, in Security and Defence, industry and public-private collaborations dominate. Table 19. Top 10 entities by number of coordination actions in Security and Defence within the Aragonese ecosystem. # Entity Name Type Country Coordination Actions 1 INSTALAZA, S.A. PRC ES 6 2CONSORCIO PARA LA GESTIÓN, CONSERVACIÓN Y EXPLOTACIÓN DEL TÚNEL DE BIELSA-ARAGNOUET Y SUS ACCESOS OTH ES 2 3FUNDACION PARA EL DESARROLLO DE LAS NUEVAS TECNOLOGIAS DEL HIDROGENO EN ARAGON REC ES 2 4 IMPLASER 99 SLL PRC-SME ES 2 5 NTT DATA SPAIN, SL PRC ES 2 6 RADE TECNOLOGIAS SL PRC ES 2 7 TITAN FIRE SYSTEM SL PRC-SME ES 2 8 ALISYS DIGITAL SL PRC-SME ES 1 9ASOCIACION ESPANOLA DE FABRICANTES EXPORTADORES DE MAQUINARIA PARA CONSTRUCCION, OBRAS PUBLICAS Y MINERIA OTH ES 1 10 Cámara Oficial de Comercio, Industria, Servicios y Navegación de Oviedo PUB ES 1 Tables 20–22 present the top 10 entities in the EU Framework program Security and Defence projects based on the same three indicators. Table 20. Top 10 entities by funding achievement in Security and Defence within the EU Framework Program projects. # Entity Name Type Country Funding Achieved (EUR) 1FRAUNHOFER GESELLSCHAFT ZUR FORDERUNG DER ANGEWANDTEN FORSCHUNG EV REC DE 45,187,957 2COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES REC FR 36,696,308 3ETHNIKO KENTRO EREVNAS KAI TECHNOLOGIKIS ANAPTYXIS REC EL 32,897,693 4 KATHOLIEKE UNIVERSITEIT LEUVEN HES BE 28,458,117 5 DEUTSCHES ZENTRUM FUR LUFT—UND RAUMFAHRT EV REC DE 23,393,434 6 ENGINEERING—INGEGNERIA INFORMATICA SPA PRC IT 21,568,866 7 AIT AUSTRIAN INSTITUTE OF TECHNOLOGY GMBH REC AT 20,893,723 8CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS REC FR 20,326,342 9 EREVNITIKO PANEPISTIMIAKO INSTITOUTO SYSTIMATON EPIKOINONION KAI YPOLOGISTON REC EL 19,582,945 10 TECHNISCHE UNIVERSITEIT DELFT HES NL 18,829,692 At the European level, Fraunhofer (EUR 45.1M) leads security research funding, followed by CEA (EUR 36.7M) and CERTH (EUR 32.9M). The top 10 entities are dominated by research institutions and universities, which collectively secure the majority of EU funding. Economies 2025,13, 218 31 of 41 At the same time, our analysis highlights the evolving nature of entrepreneurial discovery in Aragón’s transition from the 2014–2020 RIS3 to the 2021–2027 framework. We also observe that, while many entities participate in multiple projects, only a few act as consistent bridges between local and European innovation spaces. This raises concerns about the inclusiveness of Aragón’s entrepreneurial discovery process (EDP), which requires broad engagement and strong external ties to remain dynamic (Gheorghiu et al.,2016;Laranja et al., 2022;Marinelli & Perianez,2017;Szerb et al.,2020). Multi-level SNA reveals sector-specific gaps that limit discovery and suggests the need for integrative tools—like inclusive funding calls and consortium-building programs—to diversify participation and sustain learning (Karo et al.,2017;Szerb et al.,2020). Bringing in actors from outside Aragón helps local stakeholders tap frontier expertise and fresh ideas, boosting entrepreneurial discovery and keeping innovation paths adaptive and outward-looking. These findings support the broader shift toward “S3 2.0,” which emphasizes sustainability, inclusiveness, and resilience (Buyukyazici,2023;European Committee of the Regions et al.,2023;Foray,2023). Well-connected areas like Energy and Green Fuels already embed environmental goals, while fragmented sectors lag behind. Metrics like high closure or uneven centrality can guide targeted action to strengthen underperforming areas (Arranz et al.,2022;Arranz & Arroyabe,2023). Inclusion also matters. Emerging domains like Security and Defence lack the actor density and bridging entities found in more mature sectors, echoing patterns in other developing regions (Yue et al.,2024). Broadening participation—from SMEs to civil society and local administrations—can enrich innovation processes and build agility (Torre,2019). Collaborative calls that prioritise underrepresented actors could help bring more diverse knowledge into play. Finally, resilience is now essential. Regions must anticipate economic or environmental shocks. Our SNA framework supports this by identifying over-dependence on single actors and enabling policy rebalancing. For instance, in Security and Defence, leadership is overly concentrated among a few firms. In contrast, in Advanced Technologies, the challenge lies in reducing central actor dominance and promoting cross-sector partnerships (Barbero et al.,2022;Janošec et al.,2024;McCann,2023;Moujaes,2024). These insights align with the gaps flagged in Aragón’s 2021–2027 S3 strategy: fragmented R&D efforts, limited inter-institutional collaboration, and insufficient public support. Despite the influence of major institutions, weak internal ties (as shown in Table 5) suggests that overlapping competencies and dispersed efforts may be diluting their overall collective impact (McPhillips,2020). This fragmentation—combined with low outreach capacity and uneven access to funding—especially affects SMEs and peripheral areas. The fact that nearly 60% of local organisations report receiving no public R&D support highlights the need for stronger intermediary networks and more accessible innovation instruments(Rossoni et al.,2024). The third research question expands the focus to both local and cross-border collaborations, exploring how external knowledge flows and transnational ties drive regional growth. By benchmarking Aragón’s innovation ecosystem against large EU Framework Program projects without Aragonese partners, the study highlights how external actors—often brokers or gatekeepers—shape high-impact networks. Understanding why Aragonese entities are absent from these consortia can help policymakers connect the region to top-tier European R&D efforts. The analysis unfolds across three fronts: (i) comparing Aragón’s ecosystem with major EU R&I projects lacking local involvement; (ii) generating policy recommendations from the structural insights in this study; and (iii) addressing S3 bottlenecks that hinder alignment between Aragón’s innovation potential and long-term goals. Economies 2025,13, 218 32 of 41 Our comparison spans 2464 projects in Aragón and 33,933 EU-funded projects ( 2014–2023 ). Despite receiving 12 times less funding (EUR 5.03B vs. EUR 57.36B), Aragón has more unique participants (8233 vs. 5741). This reflects a fragmented ecosystem dominated by small, short-term collaborations, unlike the EU’s dense, repeated partnerships—a common issue in regions facing coordination failures (Isaksen et al.,2022). Moreover, Aragón channels more funding to private firms (PRC) and intermediaries (OTH), reinforcing its industrial focus, while EU projects favour academic and research institutions. This imbalance suggests underdeveloped scientific leadership and limited engagement in high-risk R&D (Isaksen & Trippl,2016). From policy implications standpoint, findings highlight both risks and opportunities. Aragón’s broad but shallow participation structure points to untapped potential: stronger, repeated collaborations with EU consortia could boost both visibility and impact. However, to do so, knowledge-transfer mechanisms need to improve links between universities and large firms. Aligning with S3 2021–2027 goals, such measures would help reinforce strong sectors (e.g., Advanced Technologies) while activating emerging areas like Hydrogen or Water through strategic international engagement. Benchmarking network metrics like fragmentation or density against centralised EU networks can guide targeted policy interventions. These might include dedicated calls, matchmaking platforms, or incentives to integrate local actors into robust, pan-European partnerships. In doing so, regional funding instruments would better align with EU priorities, addressing a key RIS3 implementation challenge (Esparza-Masana,2022). Finally, this section applies those insights to two contrasting S3 areas: Advanced Technologies, a mature sector, and Security and Defence, an emerging one. By tailoring policy actions to each domain’s structural features, Aragón can position itself more strategically in European innovation ecosystems and deliver on its S3 2021–2027 ambitions. The Advanced Technologies network in Aragón features a strong set of actors—ITA, UNIZAR, CIRCE, and AITIIP—closely connected with local firms and intermediary organizations. However, while these local clusters perform well in national calls, large-scale European projects remain led by top research institutions like Fraunhofer, IMEC, and CEA. This gap in project scale and funding highlights the need to increase regional network density and strengthen Aragón’s presence in major EU consortia, addressing persistent weak ties among quadruple helix actors (Rossoni et al.,2024). Anchor institutions such as ITA, UNIZAR, and CIRCE stand out for their extensive regional and international collaborations, including with Fraunhofer, CERTH, and RINA. Their high betweenness centrality shows strong potential for coordinating consortia and managing resources. However, limitations remain: industrial actors often engage via cluster-led structures rather than long-term partnerships, and key Aragonese recipients attract less funding than their European counterparts. This dynamic echoes institutional fragmentation in other metropolitan RIS (Tödtling & Trippl,2005). To strengthen the ecosystem, several policy measures are recommended. First, strategic calls should target high-value areas (e.g., Industry 4.0, robotics, semiconductors) and prioritise consortia that combine multiple regional R&D centres with international partners. Second, despite a comparatively modest academic base, represented largely by UNIZAR, these actors can achieve more by expanding living labs and collaborative testing spaces with firms. Scaling initiatives like the Aragón European Digital Innovation Hub (AEDIH, 2017) and connecting them with EU leaders (EUA,2019) offers an immediate opportunity. These measures directly confront the RIS3 challenge of insufficient monitoring and feedback loops by encouraging continuous, practice-oriented experimentation and learning. Internationalisation is also key. While ITA and CIRCE have global links, many SMEs and tech firms remain disconnected from major European projects. This gap could be Economies 2025,13, 218 33 of 41 addressed through targeted missions, matchmaking events, or shared proposals with institutions such as Chalmers, TU Eindhoven, or VTT (Ferraro & Iovanella,2017). Creating an “Advanced Technologies International Office” within the regional government would help local organizations track and engage with emerging EU opportunities—tackling organizational thinness and enhancing absorptive capacity (Isaksen et al.,2022). Finally, boosting local leadership in consortia would align technical capacity with strategic influence. Although ITA is a leading participant, it rarely coordinates EU projects. Introducing incentives for regional actors who lead international proposals—along with dedicated training in consortium leadership and EU project management—could close this gap (López-Rubio et al.,2020). Altogether, these actions—enhancing local collaboration, improving academic– industry ties, and deepening international engagement—can elevate Aragón’s role in this area, positioning the region as a competitive, high-impact innovation hub. Aragón’s Security and Defence network is mainly driven by industry, with companies like INSTALAZA and ARPA leading in both coordination and funding. These firms have capabilities aligned with emerging EU priorities, such as dual-use technologies and border security solutions (European Commission,2024). However, unlike in Advanced Technologies, public research and academic involvement remains limited. At the European level, major institutions like CEA, Fraunhofer, and KU Leuven dominate consortia, making it hard for new or smaller players to enter. While UNIZAR and ITA act as occasional bridges, only three entities exhibit enough betweenness centrality to support wider network integration. This configuration presents both opportunities and constraints. On the one hand, firms like INSTALAZA and ARPA already show strong technical capacity and could align with European initiatives if better connected. Stable, high-closure clusters indicate internal cohesion and readiness for collaborative innovation. On the other hand, few bridging nodes and stringent security regulations restrict access—especially for SMEs lacking the credentials or networks to enter the defence domain. These barriers reflect a key S3 implementation gap: limited engagement beyond initial planning phases hampers the growth of emerging sectors (Laranja et al.,2022). To strengthen this ecosystem, policymakers should consider a targeted “Defence and Dual-Use Innovation” program (Csernatoni,2021), to attract new entrants through dedicated grants and lower entry thresholds. Cross-sector demonstration projects—combining defence needs with AI, hydrogen, or robotics—could foster new collaborations and broaden the sector’s base. Hosting international research chairs or offering secondments for European experts would also raise the profile of local actors. A joint Project Office focused on EU defence calls could ease participation, reduce administrative hurdles, and support local leadership in multinational proposals. Together, these measures—engaging first-time participants, promoting interdisciplinary pilots, strengthening global ties, and offering practical support—would transform Aragón’s Security and Defence network into a more connected, competitive, and outwardlooking ecosystem. They also directly address the RIS3 bottlenecks of fragmentation, limited outreach, and institutional inertia, offering a path toward a more responsive and strategically aligned regional innovation system. 6. Conclusions This study builds on our previous analysis (Rodríguez Ochoa et al.,2025) of Aragón’s innovation ecosystem under the 2014–2020 RIS3 strategy, expanding it in four key areas: (i) a deeper analysis of cohesion and centrality across regional, national, and EU levels by specialisation area; (ii) examination of collaboration patterns among Aragón’s key institutions (UNIZAR, ITA, CIRCE, and AITIIP); (iii) identification of leading actors in EU Economies 2025,13, 218 34 of 41 Framework Program projects; and (iv) assessment of how S3 policies can address persistent challenges, including fragmented R&D capacity, limited cooperation, and technology transfer bottlenecks. Our results show a highly concentrated funding landscape: just four areas—Energy and Green Fuels, Health and Wellbeing, Agri-Food, and Advanced Technologies—capture about two-thirds of all competitive funding. Meanwhile, emerging fields like Hydrogen, AI, Security and Defence, and Water remain marginal, representing only 10% of total project activity. These imbalances stem from both strong legacy networks and persistent barriers in newer domains, such as limited outreach, weak funding alignment, and low technology readiness. Cohesion metrics reveal a split structure: larger areas host many participants but remain poorly connected, while smaller ones are denser but too limited in scale. Centrality analysis confirms that mature areas benefit from multiple influential hubs, while emerging ones depend on just a few actors, which restricts knowledge diffusion and entrepreneurial discovery. Focusing on Advanced Technologies and Security and Defence, the analysis identifies ITA and UNIZAR as key intermediaries in the former, supported by CIRCE, AITIIP, and firms like BSH. However, most of these actors tend to collaborate rather than lead large consortia. In contrast, Security and Defence is led by firms like INSTALAZA and ARPA, forming a tightly closed, industry-centric network with minimal academic involvement and limited reach. Across both domains, SMEs show weak integration—underscoring the need for targeted public–private partnerships and support mechanisms. Meanwhile, although institutions like UNIZAR, CIRCE, ITA, and AITIIP act as key “knowledge anchors” locally and internationally, they rarely collaborate directly. This siloing—driven in part by funding schemes favouring business–public pairings—dilutes their collective impact. Considering the theoretical contributions, this study advances RIS3 theory by showing why place-based, differentiated strategies matter. Our network analysis reveals the pitfalls of “one-size-fits-all” approaches, highlighting the structural contrasts between mature sectors (e.g., Energy) and newer ones (e.g., Defence). Metrics like closure, density, and centrality can guide policy design to better harness local assets and fix domain-specific weaknesses. The findings also question the inclusiveness of the entrepreneurial discovery process, showing that only a few institutions truly bridge local and EU networks, leaving many actors at the periphery. Broader participation—via consortium-building and more inclusive funding calls—is essential. Finally, by applying a sustainability–inclusiveness– resilience lens, this work shows how social network analysis can serve as a continuous diagnostic tool for adaptive, data-driven governance. In doing so, it provides a roadmap for transforming static RIS3 plans into dynamic, evidence-based strategies. With regard to the policy implications, benchmarking Aragón’s 2464 locally led projects against 33,933 EU initiatives reveals several strategic directions to strengthen its innovation system. To increase network density and reduce fragmentation, future challenge-driven calls should require consortia that connect multiple Aragonese R&D centres, SMEs, and at least one top-tier European partner. This structure would not only diversify partnerships but also stimulate deeper collaboration across the regional ecosystem. To support emerging fields like Security and Defence and Hydrogen, the region would benefit from launching an Advanced Technologies International Office and a dedicated Defence and Dual Use Innovation program. These initiatives could help smaller firms join EU consortia by offering preparatory grants, targeted matchmaking with international actors, and legal assistance throughout the application process. Embedding social network analysis into S3 governance would create a much-needed feedback mechanism to adjust funding instruments dynamically, prevent institutional Economies 2025,13, 218 35 of 41 inertia, and align public support with real network structures and policy goals. At the same time, building local capacity remains critical. Training programs in EU consortium leadership—particularly for key players such as ITA—and the expansion of collaborative “living lab” models would encourage broader participation in underrepresented specialisation areas, especially Hydrogen and Water. By taking these coordinated steps, Aragón can transition from a fragmented and uneven innovation landscape to a more integrated, resilient, and outward-facing ecosystem, which is better aligned with its smart specialisation strategy and more capable of seizing the opportunities of the European R&D landscape. While this study offers valuable insights, it also has some limitations that point to future research opportunities. By focusing exclusively on competitive public calls, it overlooks private R&D investments and informal innovation activities—both of which play a key role in shaping collaboration, especially in industry-led sectors. Our social network analysis, based on project-level data, may also miss the influence of actors who contribute through spinouts, advisory roles, or contract research outside formal partnerships. Aligning projects with the S3 Aragón 2021–2027 specialisation areas offers consistency but may exclude emerging fields—like AI-enabled technologies, biotechnology, or quantum computing—that have yet to gain full policy recognition. Expanding the analysis to include patent data, scientific publications, or foresight studies could better capture these nascent trajectories. Similarly, while the comparison with Horizon 2020 and Horizon Europe strengthens external validity, incorporating other EU funding programs—such as the European Defence Fund or LIFE Programme—could refine the picture and help identify the structural barriers limiting Aragonese participation in pan-European consortia. Building on this work, future research should track how Aragón’s network metrics evolve over time in response to S3 2021–2027 interventions, helping to clarify causal relationships between specific policy actions and changes in cohesion or centrality. Combining this longitudinal analysis with qualitative interviews—from policymakers to SMEs and civil society—would add depth, revealing the motivations and obstacles behind real-world partnerships. Additionally, exploring unfunded or unsuccessful consortia could uncover untapped collaboration potential and procedural hurdles that block promising initiatives. Together, these mixed methods could offer more practical, evidence-based insights for refining and adapting smart specialisation policies. Author Contributions: Conceptualization, D.R.O. and N.A. and M.F.d.A.; methodology, N.A. and M.F.d.A.; software, D.R.O.; validation, N.A. and M.F.d.A.; formal analysis, D.R.O.; investigation, D.R.O.; resources, D.R.O.; data curation, D.R.O.; writing—original draft preparation, D.R.O.; writing— review and editing, N.A. and M.F.d.A.; supervision, N.A. and M.F.d.A.; project administration, D.R.O.; funding acquisition, D.R.O. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Directorate General for Industrial Promotion and Innovation of the Aragon Government. Informed Consent Statement: Not applicable. Data Availability Statement: European projects data is available in a publicly accessible repository of the European Commission at EU Funding & Tenders Portal in the following link: https://ec .europa.eu/info/funding-tenders/opportunities/portal/screen/opportunities/projects-results ?order=DESC&pageNumber=1&pageSize=50&sortBy=title&isExactMatch=false (accessed on 8 May 2025). At the Spanish level, the data can be found in the open data repository from the centre for industrial technology development (CDTI for its initials in Spanish), through the following link: https://www.cdti.es/datos-abiertos-creditos-subvenciones-y-lineas (accessed on 8 May 2025) and in the calls repository of the Spanish Ministry of Science, Innovation and Universities through the Economies 2025,13, 218 36 of 41 following link: https://www.ciencia.gob.es/home/Convocatorias (accessed on 8 May 2025). At the regional level, the information about projects can be found in the Aragón government’s official database (https://www.aragon.es/-/next-generation-eu-convocatorias#anchor1, accessed on 8 May 2025) and from the database of the Aragón Development Institute (IAF for its initials in Spanish) https://www.iaf.es/ayudas (accessed on 8 May 2025). Conflicts of Interest: The authors declare no conflicts of interest. Abbreviations The following abbreviations are used in this manuscript: AITIIP AITIIP Foundation ALIA Aragonese Logistic Cluster ARPA Mobile Field Units ARPA CAAR Automotive and Mobility Cluster of Aragón CDTI Centre for Industrial Technology Development CEA French Alternative Energies and Atomic Energy Commission CERTH Centre for Research & Technology, Hellas CIRCE CIRCE Foundation CNR Italian National Research Council CNRS French National Centre for Scientific Research CORDIS Community Research and Development Information Service CSIC Spanish National Research Council EC European Commission EDP Entrepreneurial Discovery Process EU European Union FP Framework Programme GDP Gross Domestic Product GVA Gross Value Added H2020 Horizon 2020 Framework Research Programme HE Horizon Europe HES Higher education establishments entity type IAF Aragón Development Institute IMEC Interuniversity Microelectronics Centre ITA Aragón Technology Institute KU Leuven Katholieke Universiteit Leuven NIF Tax identification number OTH Sector-level organisations entity type PIC Participant Identification Codes PRC Private companies entity type PRC-SME Small and medium enterprises entity type PUB Public sector entity type REC Research organisations entity type RIS Regional Innovation Systems RIS3 Research and Innovation Strategies for Smart Specialisation RQ Research question R&D Research and Development R&I Research and Innovation S3 Smart Specialisation Strategy SME Small and Medium Enterprises SNA Social Network Analysis UN United Nations UNIZAR University of Zaragoza URF Unique Registration Facility Economies 2025,13, 218 37 of 41 Appendix A For clarity and ease of reading, supplementary details to the Materials and Methods section have been relocated to this appendix. The detail of the primary data sources for this study are shown below. • Regional Project Databases: Data on competitive public projects at the regional level were collected from official Aragón government repositories and databases, including those maintained by the Aragón Development Institute (IAF). • National Project Databases: Information regarding national-level projects involving Aragonese entities was obtained from Spain’s Ministry of Science and Innovation and other related national funding agencies, such as the Centre for Industrial Technology Development (CDTI). • European Project Databases: Project data at the European level were sourced from databases managed by the European Commission, including those for Horizon Europe, INTERREG, and LIFE programs, among others. The CORDIS database (Community Research and Development Information Service) has been particularly instrumental, providing detailed information on projects, participants, funding, and outcomes. For the Aragonese innovation ecosystem, we exclusively included projects featuring at least one participant from the Aragón region. Although the majority of these projects are collaborative public initiatives—characterized by multiple independent entities working together on shared research and innovation objectives—a small proportion of competitive projects undertaken by a single entity was also incorporated. For example, projects funded under the H2020 SME Instrument were included to reflect the performance of these entities in specific domains. For the selection of entities, those from the Aragón region were identified using their tax identification numbers (NIF in Spanish) and associated addresses. Accordingly, any external entities involved in the selected projects were also included, as they contribute directly to the Aragonese research and innovation ecosystem through collaborative efforts. Regarding the European benchmark, only projects under Horizon 2020 and Horizon Europe were considered, as these two funding schemes represent 78% of the total funding allocated within the Aragonese ecosystem. This focused selection enables a comprehensive comparison of regional performance in the context of broader European initiatives. To ensure the accuracy and relevance of the data to Aragón’s innovation ecosystem, several rigorous processing steps were undertaken: • Data Cleaning: Following the extraction of data from regional, national, and European project databases, a thorough cleaning process was implemented to remove duplicates, resolve inconsistencies, and correct inaccuracies. Entity identifiers were verified, and formatting was standardized across datasets. For Aragón-based entities, confirmation was achieved through their tax identification numbers (NIF) and registered addresses. Additionally, the dataset was cross-referenced with official regional directories and national databases—such as records from the Spanish Ministry of Science and Innovation—and, for European projects, with the European Commission’s Participant Identification Codes (PICs) and the Unique Registration Facility (URF) database. This extensive validation ensured that both local entities and their external collaborators were accurately represented. • Network Construction: Using the refined dataset, collaborative networks were constructed wherein entities are depicted as nodes and their joint participation in projects as edges. These networks, covering projects from the regional, national, and European levels (2014–2023), serve as the foundation for subsequent social network analysis aimed at revealing the structural and dynamic characteristics of the ecosystem. Economies 2025,13, 218 38 of 41 • Attribute Assignment: Each node in the network was enriched with relevant attributes to facilitate a nuanced analysis of its role and significance. Attributes included the type of entity (e.g., higher education institutions, large private companies), their project role (coordinator or participant), the participation level (regional, national, or European), the specialisation area and the funding received. • Data Validation: To maintain the integrity of the constructed networks, random samples were cross-checked against the original databases, and any identified discrepancies were promptly addressed. This validation step was critical to ensure that the networks accurately reflect the collaborative activities and relationships within Aragón’s innovation ecosystem over the specified period. References Abreu, A., & Nunes, M. (2020). Model to estimate the project outcome’s likelihood based on social networks analysis. KnE Engineering, 5(6), 299–313. 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