Economic and Societal Impacts of Publicly Funded R&D: Evidence from Project Leaders in the Czech Republic

Economic and Societal Impacts of Publicly Funded R&D: Evidence from Project Leaders in the Czech Republic | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Economic and Societal Impacts of Publicly Funded R&D: Evidence from Project Leaders in the Czech Republic Ulrike Michel-Schneider, Markéta Kühnelová, Martin Bunček, Petr Konvalinka This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7878204/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Apr, 2026 Read the published version in Future Business Journal → Version 1 posted You are reading this latest preprint version Abstract Public research and development (R&D) funding plays a crucial role in driving innovation and economic growth in Europe, supported by multilevel frameworks including EU-wide and national funding schemes. This study investigates the experiences and perceptions of participants engaged in publicly funded R&D projects administered by the Technology Agency of the Czech Republic (TA ČR), focusing on economic and societal impacts. A comprehensive survey of 203 project leaders from diverse organisations examined three key questions: preferences for project structures, expected economic and societal impacts, and barriers and enablers in realizing project outcomes. Findings indicate a distinct advantage of collaborative projects, especially university-industry collaborations, in generating significant economic benefits such as new products, market expansion, and job creation alongside societal benefits, including skills development and environmental improvements. SMEs emerged as primary drivers of commercialization, while research organisations contributed to knowledge-based outputs. Barriers such as bureaucracy, intellectual property negotiations, and certification delays constrain impact realization. The study recommends flexible, impact-driven funding mechanisms prioritizing company-led collaborations and tailored support for commercialization and societal value creation. These insights contribute to enhancing the design of public R&D funding policies to maximize innovation impact and sustainability. Research impact assessment economic impact of R&D societal impact of R&D university–industry collaboration (UIC) knowledge and technology transfer (KTT) impact indicators (KPIs) Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Public research and development (R&D) funding is considered to play a pivotal role in fostering innovation and driving economic growth across Europe (Dworak et al., 2022 ). This strategic role is prominently prioritized by the European Union (EU) ( Priorities 2024–2029 - European Commission , n.d.), through funding frameworks such as Horizon Europe ( Horizon Europe the EU’s Funding Programme for Research and Innovation , n.d.) and the European Innovation Council (EIC) ( Europe’s Flagship Innovation Programme to Identify, Develop and Scale up Game Changing Innovations , 2025), which provide substantial, targeted resources to stimulate research and innovation activities across member states. Complementing these EU-wide initiatives, individual EU member countries implement various national and regional funding schemes tailored to their specific innovation ecosystems, priorities, and capabilities, thereby creating a multilevel framework that supports robust R&D activities Public funding is accompanied by high expectations for both tangible and intangible economic and societal impacts, leading to a growing interest in comprehensively and systematically evaluating these outcomes. Recent literature converges on the conclusion that both tangible metrics (e.g., patents, commercialization, job creation, GDP contribution) and intangible metrics (e.g., knowledge spillovers, capacity building, skills development, societal well-being) are essential to capture the full spectrum of publicly funded R&D impacts (Ebersberger, 2005 ; M. Cohen et al., 2025 ; Directorate-General for Research and Innovation (European Commission), 2009 ; Georgiou et al., 2025 ; Almus & Czarnitzki, n.d.). These metrics must account for immediate outputs, medium-term outcomes, and long-term effects, reflecting the complex and dynamic nature of R&D impact pathways. Expectations and metrics reflect a rather linear view of the mechanics behind the formation of new technologies, products, or services put in the market. However, the reality is often non-linear, as there are many sources of ideas, knowledge, and other necessary resources, as well as market demand, which can or cannot exist. In economics and market theory, this is typically expressed as market failures that can be addressed through targeted aid, which may have spillover effects (Dhanora et al., 2018 ) (Nie et al., 2022 ). Additionally, there is an economic theory by Mazzucato that reflects the role of public support in creating new markets (Yencha, 2015 ) (Bagattolli et al., 2023 ). This leads to a more complex, holistic view of how to measure the impact of public funding of research and innovation activities. Viewing innovation as a linear sequence, from project initiation through support, results, innovation, and market entry, oversimplifies its complexity and neglects systemic interdependencies. Innovation systems research shows that innovation emerges through iterative, non-linear interactions among diverse actors (Lundvall, 1992 ), (Freeman, 1995 ), (Edquist, 1997 ). Beyond statistical or econometric analyses, the use of sociological and psychological methods can reveal fascinating insights. Within national contexts, funding agencies often serve as crucial intermediaries, administering public R&D programs aligned with national strategic specializations. In the Czech Republic, for instance, this role is undertaken by the Technology Agency of the Czech Republic (TA ČR), which has been orchestrating a wide range of applied research and innovation funding programs since 2009. TA ČR’s operations reflect a systematic approach to evaluation and funding, rooted in long-standing national evaluation methodologies. These methodologies incorporate multi-criteria assessments covering scientific excellence, innovation potential, and societal relevance. The objective of the paper is to deepen the understanding of the experiences and perceptions of participants involved in publicly funded R&D projects regarding the projects’ economic and societal impacts. To this end, the authors conducted a comprehensive survey targeting R&D actors financed by public programs by TA ČR, aiming to illuminate three central research questions: What types of R&D project structures (e.g., collaborative vs. individual) are preferred by participants in publicly funded R&D projects, and how do these preferences relate to the perceived potential for economic impact? What are the specific economic and societal impacts that participants expect from publicly funded R&D projects, and how do these expectations vary by organisational type and project characteristics? What are the main barriers and enabling factors identified by participants that influence the realization of economic and societal impacts from publicly funded R&D projects? These questions address crucial gaps in understanding how funding frameworks translate into perceived value and how economic and societal outcomes are envisioned by those directly engaged in project execution. The paper follows a classical structure comprising a literature review, a detailed methodology, and a critical analysis of survey results on the impact of R&D funding. The discussion situates findings within broader policy debates, while the conclusion summarizes contributions and offers recommendations for future research and practice. 2. Literature Review 2.1 Market Failure and Innovation The rationale for public R&D funding can be attributed to the market failures that hinder the efficient generation of innovation (Edler et al., 2016 ). Bryan and Williams (Bryan & Williams, n.d.) provide a comprehensive review of literature on the economics of innovation, focusing on why markets alone often fail to generate optimal innovative activity and how public policies, such as funding instruments, can address these shortcomings. Key market failures identified include knowledge spillovers, which lead to firms underinvesting in R&D because the returns from inventions cannot be fully captured, and coordination failures that prevent complementary innovations from emerging when individual innovators cannot align their efforts. Furthermore, capital market imperfections restrict financing for high-risk, long-term research, especially for smaller firms. Inefficient intellectual property rights (IPR) regimes can either discourage innovation by granting overly broad rights or fail to protect inventors adequately. Targeted public policy interventions help address these shortcomings and stimulate innovation, for example, through publicly funded research, development, and innovation (R&D&I) initiatives. Martin and Scott (Martin & Scott, 2000 ) also highlight how corresponding market failures (knowledge spillovers, coordination problems, and financing constraints) hinder innovation and justify public intervention. They emphasize that innovation dynamics differ across sectors, requiring tailored policy responses. Effective tools include R&D grants, tax incentives, and support for collaborative networks, each suited to specific industry contexts. The authors argue for a strategic, sector-sensitive approach to innovation policy to better address the underlying causes of underinvestment in technological development. Mazzucato (Mazzucato, 2015 ) argues that traditional innovation policy, which focuses on addressing market failures, is too limited to drive inclusive and transformative growth. She proposes a mission-oriented approach, where the state actively shapes and creates markets through strategic investments and coordination. Key issues include the need for directional public policy, dynamic evaluation tools, capable public institutions, and mechanisms to share both risks and rewards of innovation. Mazzucato calls for rethinking the role of government from passive fixer to active driver of innovation and economic change. Public R&D funding as a tool to correct market failure not only addresses structural barriers to innovation but also enables high-risk, long-term research that the private sector often avoids. It stimulates collaboration across industries and drives innovation in areas of strategic and societal importance. Its impact lies in shaping the direction of technological development and ensuring that both the risks and rewards of innovation are more broadly shared across society. 2.2. Institutions Guiding Innovation Monitoring A range of international and national institutions are actively engaged in monitoring innovation, particularly with respect to publicly funded R&D. At the global level, the OECD plays a central role through its widely adopted Frascati (OECD, 2015 ) and Oslo Manuals (OECD & Eurostat, 2018), which set the standard for collecting and analysing R&D and innovation data. The European Commission, notably through DG Research and Innovation and the Joint Research Centre (JRC), has developed comprehensive tools like the Innovation Scoreboard, Key Impact Pathways under Horizon Europe, and Smart Specialisation Strategies (S3) to assess regional and sectoral innovation performance across the EU. In developing countries, the UNESCO Institute for Statistics supports the collection of R&D data to enhance comparability and inform policy capacity. The World Bank complements this by providing Science, Technology, and Innovation (STI) indicators and toolkits tailored to institutional diagnostics and policy design in emerging economies. National-level monitoring is carried out by statistical offices and innovation agencies, which produce detailed evaluation reports aligned with both domestic priorities and international standards. These institutions collectively enable evidence-based innovation policy through standardized metrics, dynamic assessment frameworks, and region-specific insights. As summarized in Table 1 , several national and global institutions have proposed guidelines for designing and monitoring innovation. Table 1 National and International Institutions guiding innovation monitoring. Institution Frameworks Focus Use/Application National Innovation Agencies Impact evaluation reports Country-specific innovation and public funding measurement Adapted to national policy priorities National and European Statistical Offices National R&D statistics Country-specific statistics connected to Eurostat ( Home - Eurostat , n.d.) Linked with European standards European Commission Horizon Europe KIPs ( RESEARCH AND INNOVATION - Horizon Europe Monitoring and Evaluation Framework , n.d.), (European Commission. Directorate General for Research and Innovation. et al., 2022), Innovation Scoreboard (Reid & Markianidou, 2025 ), RIS3 (Directorate-General for Regional and Urban Policy (European Commission) et al., 2012)20/10/2025 14:25:00 Impact assessment of EU R&I programmes and regional innovation strategies Monitors Horizon Europe, cohesion policy, smart specialisation strategies OECD Frascati Manual (OECD, 2015 ), Oslo Manual (OECD & Eurostat, 2018) Standardized methodologies for R&D and innovation data collection Used globally for measuring public R&D investment, innovation inputs and outputs UNESCO Institute for Statistics Global R&D Statistics (based on Frascati Manual) (UNESCO Institute for Statistics, 2019 ) Innovation and R&D metrics in developing countries Supports international comparisons and policy capacity building World Bank Practitioner’s Guide to Innovation Policy (Frias et al., 2020 ), STI indicators (Watkins & Ehst, 2008 ) Policy making in developing countries Used for policy design, institutional diagnostics, capacity development 2.3 Challenges in Measuring Innovation Impact A major challenge in studying the outcomes of publicly funded initiatives lies in accurately measuring their processes and impacts on innovation. Bryan and Williams (Bryan & Williams, n.d.) emphasise that capturing knowledge spillovers is challenging, as much of the diffusion occurs informally and is not well-documented in data sources such as patents or citations. Also, mapping the timing and extent of diffusion across industries and geographies is complex, with long lags and inconsistent observability. Standard metrics such as R&D spending or patent counts are criticized for failing to reflect the true quality or economic value of innovations. Linking specific innovations to broader economic outcomes like productivity growth or inequality is methodologically challenging, due to issues like endogeneity and unobserved variables, which complicate causal inference. Attribution is complex, as innovation outcomes typically emerge from the interplay of multiple actors and factors, making it difficult to isolate the effects of a specific intervention (Gault, 2020 ). Gault states that the long-time lags between investment and observable results hinder timely assessment. At the same time, many outputs, such as knowledge creation or institutional change, are intangible and hard to quantify. Innovation processes are also dynamic and nonlinear, meaning traditional linear evaluation models often fall short. Standard measurement tools, like patent counts or R&D expenditure, may introduce biases by overlooking broader societal or environmental impacts and underrepresenting less technology-intensive sectors. Furthermore, the highly context-dependent nature of innovation and the frequent absence of reliable counterfactuals further complicate efforts to draw robust conclusions about impact. Cohen et al. (M. Cohen et al., 2025 ) identify four key methodological challenges in measuring the impact of university-industry collaborations (UICs). First, the multidimensional nature of impact, ranging from tangible to intangible, from short-term to long-term, and from direct to indirect effects, makes consistent measurement complex. Second, there is a causal attribution challenge, as it is difficult to determine whether observed outcomes (e.g., increased sales) result from the UIC itself or other external factors. Third, the identification of impacts is hindered by subjective perceptions and differing expectations across individuals and institutions, especially regarding future societal benefits. Lastly, data limitations pose a significant barrier due to insufficient or inconsistent data collection practices, low response rates, and contextual factors (e.g., geographical or institutional) that limit data availability and reliability. Together, these challenges complicate the evaluation and understanding of UIC outcomes. 2.4 Evaluation Methods for R&D Impact Despite the complexity and diversity of challenges in measuring innovation impact, funding agencies must implement metrics to obtain feedback on their investments, meet stakeholder requirements, and improve programme design. Scientific literature and EU policy documents explore and propose a number of evaluation methods. For example, Choen et al. (M. Cohen et al., 2025 ) identified six main categories of impact resulting from university–industry R&D collaborations: intellectual (e.g., knowledge creation, learning), economic (e.g., wealth generation, funding), technological (e.g., new technologies, patents), environmental (e.g., pollution reduction), social (e.g., regional development, job creation), and strategic (e.g., reputation, future collaborations). These impacts operate at micro (individual), mezzo (organisational), and macro (societal) levels and can be tangible or intangible, direct or indirect, as well as short- or long-term. European Commission’s Expert Group report (Directorate-General for Research and Innovation (European Commission), 2009 ) on knowledge transfer metrics aims to harmonize and improve the comparability of data regarding knowledge transfer activities from public research organisations (PROs) to business and society across Europe. The group identifies a set of core indicators for regular monitoring: research agreements, invention disclosures, patent applications and grants, executed licenses, licensing income, and spin-offs. Supplementary metrics, such as knowledge transfer with small and medium-sized enterprises (SME), regional engagement, and patent utilization, are also recommended for more detailed monitoring. Performance indicators should be normalized by research expenditure or personnel for robust comparison. While the current focus is on patenting and licensing, reflecting the activities of the Knowledge and Technology Transfer Office (KTTO), the report acknowledges the need to capture broader knowledge transfer mechanisms in the future. Benoit et al. (Directorate-General for Research and Innovation (European Commission) et al., 2025 ) suggest a knowledge complexity approach to complement traditional metrics. The complexity framework analyses the structure and relationships within innovation ecosystems, helping policymakers understand technological capacity, diversification potential, and strategic development opportunities. Roper et al. (Roper et al., 2004 ) investigate the ex-ante and ex-post evaluation methods, two distinct approaches used to assess the impact of publicly funded R&D projects. Ex-ante evaluations are conducted before the implementation of a project or investment and aim to forecast its potential outcomes, benefits, and risks. This forward-looking assessment helps decision-makers determine whether to proceed with funding and guides resource allocation by posing "what-if" scenarios. It relies heavily on secondary data and predictive models to estimate expected impacts and benefits. In contrast, ex-post evaluations take place after project completion, focusing on measuring and analysing the actual outcomes, effectiveness, and impacts of the funded research. These retrospective assessments use primary data collected during or after the project, providing evidence of realized benefits, lessons learned, and policy effectiveness. While ex-ante evaluations are preventive and guide strategic planning, ex-post evaluations validate results and improve future project design by reflecting on real-world achievements and shortcomings. Spaapen & van Drooge’s (Spaapen & Van Drooge, 2011 ) framework thus offers a dynamic, process-oriented lens, shifting away from rigid before–and–after dualities, and toward recognizing interaction patterns that meaningfully contribute to impact, whether evaluated prospectively (ex-ante) or retrospectively (ex-post). Petrin (Petrin, n.d.) concludes that pure econometric evaluations of government R&D support should be complemented by long-term ex-post studies and in-depth qualitative case analyses to capture both measurable results and contextual insights. Qualitative versus quantitative methods further offer approaches to evaluating R&D impacts, providing complementary insights (Georgiadis et al., 2012 ). Quantitative methods use numerical data and statistical analysis to measure inputs, outputs, and outcomes of R&D objectively. Common examples include bibliometric analysis (e.g., citations, patents), cost-benefit analysis, econometric modelling, and commercialization metrics. These approaches provide clear, comparable indicators of performance and economic impact. Qualitative methods, in contrast, focus on understanding the context, processes, and subjective aspects of research impact that numbers alone may miss. Techniques such as case studies, interviews, expert panels, and narrative analysis explore the added value, effectiveness, and unexpected outcomes of R&D investments. Increasingly, combining qualitative insights with quantitative data (mixed method) is recommended for a well-rounded assessment of publicly funded research impact. 2.5 Key Factors Influencing Impact One key factor influencing the impact of publicly funded R&D projects is the role of collaboration and networks, conceptualized in the Triple Helix model by Etzkowitz and Leydesdorff (Etzkowitz & Leydesdorff, 2000 ). This model highlights the dynamic interaction between universities, industry, and government as essential for innovation and knowledge exchange. Collaboration increases the innovation output and effectiveness of public funding by fostering the sharing of resources, knowledge, and capabilities among partners, thus enhancing the overall impact. Empirical studies have shown that collaboration combined with public funding leads to higher innovation output, especially when collaborative innovation activities are actively supported within funding schemes (Ebersberger, 2005 ) (Cunningham & Gök, 2012 ) (Czarnitzki et al., 2004 ) (Hottenrott & Lopes-Bento, 2014). Networks help overcome financial constraints and stimulate private sector R&D investment, particularly benefiting smaller firms and SMEs by leveraging additional resources and facilitating access to markets and expertise (Hilmersson & Hilmersson, 2021 ). Another critical influence is the absorptive capacity of organisations, a concept developed by Cohen and Levinthal (W. M. Cohen & Levinthal, 1990 ), which refers to the ability of firms or institutions to recognize, assimilate, and apply external knowledge. Effective use of publicly funded R&D outputs depends significantly on this capacity, enabling the translation of research findings into innovative products, processes, or services. Governance and project management are vital in publicly funded R&D projects to ensure alignment of objectives, efficient resource allocation, and coordination through the project lifecycle. Flexible governance impacts partner integration, project costs, and quality (Pisano, 2010 ). Strong governance and minimizing bureaucracy improve performance and motivation, especially in novel collaborations (Cunningham & Gök, 2012 ). Adaptive governance models help consortia and funding agencies achieve better innovation outcomes by evolving through project phases (Kim et al., 2025 ). Well-designed funding instruments and institutional settings play a crucial role in shaping outcomes by promoting cooperation, flexibility, and responsiveness to specific needs. European University Alliances, for instance, implement shared management structures and joint governance bodies that foster sustainable collaboration across universities, pooling resources and aligning strategic goals effectively (Estermann & Pruvot, 2021 ). Regional innovation policies in Europe emphasize place-based approaches that tailor UIC frameworks to local institutional contexts, enhancing the exploitation of R&D results for regional competitiveness (Morisson & Pattinson, 2020 ). Moreover, OECD reports highlight a shift towards hybrid governance models combining formal institutional arrangements with relational trust-building mechanisms to strengthen university-industry innovation partnerships across Europe (OECD, 2019 ). 2.5 Gaps in Existing Literature Existing literature on the impact of publicly funded R&D projects reveals several important gaps. First, there is a lack of studies that capture the perspectives of different stakeholders involved, which limits our understanding of the diverse impacts and challenges. Second, soft impacts such as organisational learning, trust-building, and stakeholder engagement remain underrepresented despite their significant role in sustaining innovation processes. Finally, there is a shortage of comparative studies across different disciplines and sectors, which restricts insights into how funding effectiveness and impact mechanisms vary in various research contexts. Addressing these gaps is crucial to developing a more nuanced and comprehensive evaluation framework for public R&D investments. 3. Methodology The applied methodology follows a Survey Study Method Plan (SSMP) as outlined by Creswell and Creswell (Creswell & Creswell, 2018 ) and is presented in detail in the subsequent section. 3.1 Survey Design The purpose of the survey is to collect and evaluate empirical data on the impact of publicly funded R&D projects, based on a representative sample of project participants, in particular, project leaders. Quantitative research in the form of a survey study was selected due to its greater capacity to capture the breadth and diversity of collaboration preferences, barriers, and outcomes. The survey consisted of an introductory section explaining the purpose of the study, followed by four main sections, each comprising multiple items designed to capture different aspects of the project experience and impact. These included: General information: Demographic questions aimed at identifying the institutional affiliation, organisational size, and role of the respondent (5 questions) Project structure: Indication of the project type they had been involved in (1 question), followed by targeted questions depending on whether the project was collaborative (6 questions) or non-collaborative (3 questions Impact of funding: Assessed perceived outcomes and included economic impact (7 questions), societal impact (3 questions), and general impact (2 questions) Additional perspectives: Questions were included to capture broader reflections and suggestions for improvement (5 questions). Questions were designed using primarily multiple-choice and Likert-type scale questions (Matas, 2018 ), but also some open-ended questions. Most questions permitted multiple responses. The questionnaire was conducted cross-sectionally, specifically capturing data from participants at one point in time (Creswell & Creswell, 2018 ). Participants could choose between the Czech and English languages to accommodate an inclusive target audience. Participation in the survey was voluntary and anonymous. No personal data was collected, and informed consent was obtained at the beginning of the questionnaire. The study adhered to general ethical standards for social science research. The definitions of the organisation types used in this study are based on the Commission Regulation (EU) No 651/2014 ( Regulation − 651/2014 - EN - General Block Exemption Regulation - EUR-Lex , n.d.). 3.2 Population and Sample The sampling frame (Groves et al., 2011 ) for this study was defined as participants involved in R&D projects funded by the TA ČR. These individuals represented organisations, including academic institutions, research institutes, SMEs, and large corporations. The eligible population was involved in projects concluded between 2022 and 2023 that had entered the phase of implementing project results. Projects involving the target population were funded by one or more of the following TA ČR programmes: TREND, DOPRAVA 2020+, PROSTŘEDÍ PRO ŽIVOT, THÉTA, ÉTA, NCK, DELTA, GAMA, and ZÉTA. Based on TA ČR’s records, the overall population of individuals involved in these funded R&D projects was identified as 13,667. From this population, a group of 2,065 registered project leaders was identified. This target group was considered most appropriate for survey distribution, as project leaders typically have a comprehensive understanding of the project’s goals, execution, and outcomes and could provide a comprehensive view. Stratification in terms of represented organisations took place. It was important that all relevant organisations were represented in this survey, and bias was reduced. Of this project leader group, 1,952 persons received the invitation, making it the final target sample. The remaining contacts (113) were excluded due to invalid contact information. In total, 205 respondents took part, of which two were disqualified, either because the survey was insufficiently completed or the respondent indicated that their organisation had not received public funding, a prerequisite for the survey. This resulted in a final dataset of 203 valid responses. 3.3 Instrumentation The survey was developed in and shared through LimeSurvey ( LimeSurvey — Free Online Survey Tool , n.d.), an online tool frequently used by TA ČR. Dissemination took place via targeted email, allowing for an economic, timely, and standardized distribution both for the participants and the researchers (Lim, 2024 ). Participants could conveniently complete the questionnaire on desktops, tablets, or mobile devices, while researchers could extract reliable raw data in the form of Excel tables and a LimeSurvey-generated analysis. The survey was pilot tested among researchers and members of TA ČR to ensure the correct spelling, readability, and flow of the questions, as well as the technical precision of the survey. The survey responses were monitored regularly to ensure an adequate response rate. A follow-up reminder email was sent two weeks after the initial distribution to encourage additional participation. 3.4. Variables of the Study This study examines the impact of publicly funded R&D projects by analysing responses from project leaders involved in TA ČR-funded initiatives. The variables were structured into independent, dependent, and control categories to support a clear analytical framework (Creswell & Creswell, 2018 ). Independent variables include project characteristics such as the type of project (collaborative or non-collaborative), organisational affiliation (e.g., academia, research institutes, SMEs, or large corporations). The dependent variables focus on the impact of the projects and are grouped into three domains: economic, societal, and general impact. Economic impact includes indicators such as product or service development, market expansion, and job creation. Societal impact refers to contributions to public welfare, sustainability, or skills creation. General impact captures broader outcomes, including strategic alignment and overall satisfaction with the public funding received. All impact variables were measured using Likert-scale items or multiple-choice and open-ended questions. 3.5 Data Analysis The survey was open for four weeks and generated 205 responses, of which 203 met the criteria for completeness and eligibility and were included in the analysis. A quantitative analysis method was employed, with the collected data examined using both descriptive and inferential statistical techniques to identify patterns, correlations, and group-specific differences (Creswell & Creswell, 2018 ). Descriptive statistics allowed for summarising central tendencies and distributions, while inferential techniques enabled testing for group differences and relationships between variables, ensuring that the findings are not only illustrative but also statistically robust. Furthermore, qualitative data in the form of open-ended survey responses were analysed using coding methods. The analysis included a demographic overview of respondents, their project roles, as well as a comparison of perceived barriers and other aspects. Furthermore, an assessment of the projects’ economic and societal impacts, and recommendations for improving publicly funded R&D initiatives were assessed. The analysis included comparisons of collaborative projects versus individual projects, as well as responses of the different organisation types. Data processing was conducted manually using Google Sheets and Microsoft Excel, whereby some categories, such as coding results, were cross-referenced and validated with the support of AI-based tools (Gemini, ChatGPT, or Perplexity) to ensure robustness and consistency of interpretation. An independent-samples two-tailed Welch t -test assuming unequal variances was conducted in Microsoft Excel to analyse two outcome categories, barriers and project-related aspects, in order to determine whether significant differences exist between collaborative and individual R&D projects (Rochon et al., 2012 ) (Hutcheson & Brown, 2024 ). Corresponding p -values and Cohen’s d effect sizes were computed (Lenhard & Lenhard, 2017 ). Given the targeted selection of respondents of experts and project leaders from research organisations and universities, research institutions, SMEs, and large enterprises, the achieved sample can be considered sufficiently representative for the purposes of this study. Nonetheless, potential sources of bias must be acknowledged. Response bias may arise from the higher likelihood of participation among more engaged or successful actors, while non-response bias may limit the visibility of perspectives from less active or less resourced organisations. These limitations are typical of expert surveys but are mitigated in this study by the diversity of organisation types and roles represented in the final dataset. 3.6 Interpreting Results The interpretation of survey results was guided by both descriptive and comparative analysis. Descriptive statistics (means, percentages, distributions) were used to summarize the overall responses and highlight central tendencies across different organisation types. Comparative analysis was employed to examine differences between collaborative projects and individual projects, as well as across organisational categories. Hereby, the percentage of answers by organisation type compared to the total responses, or the mean of rating questions, was frequently used. As for the Welch t -test, p < 0.05 indicates a statistically significant difference between group means, while p ≥ 0.05 indicates no statistically significant difference. As the test is two-tailed, significance applies to differences in either direction; the p -value is computed with Welch’s degrees of freedom and is sensitive to sample size, meaning small effects can reach statistical significance in large samples. Cohen’s d quantifies the effect size, with magnitude interpreted as | d | < 0.20 = negligible, 0.20 ≤ | d | < 0.50 = small, 0.50 ≤ | d | < 0.80 = medium, and | d | ≥ 0.80 = large. For unequal variances, d is calculated using the pooled average standard deviation. Open-ended responses were subjected to qualitative coding, whereby statements were grouped into thematic categories (e.g., barriers to commercialization, perceptions of societal impact). The interpretation approach thus combines statistical rigor with thematic analysis, ensuring that the results reflect both measurable trends and the nuanced perspectives of participants. This mixed approach provides a balanced view, capturing both the generalizability of quantitative findings and the contextual richness of qualitative responses. 4. Results 4.1 Respondent Profile The targeted survey with 1,952 participants resulted in 203 valid responses deriving from project leaders coming from research organisations/universities (26,6%), research institutions (14%), SMEs (42,3%), large corporations (14,9%), and others (2,3%) who participated in R&D projects funded by TA ČR. Some respondents hold multiple positions, resulting in a total of 222 affiliations. The distribution of affiliations can be found in Table 2 , which shows that SMEs make up the strongest group. This result is consistent with the distribution of TA ČR grants recipients. Table 2 Distribution of participating organisations Distribution of participating organisation in number of participants Type of Organisation Research Organisation – University Research Institute SME Large Enterprise Other Total Total count 59 31 94 33 5 222 Total % 26.6% 14.0% 42.3% 14.9% 2.3% 100% Table 3 describes the types of TA ČR programmes to which the survey respondents have participated. A total of 369 selections were made, showing clearly the multiple participations in projects. As reflected in TA ČR statistics, TREND is the largest programme, allowing for the most projects to be generated. Table 3 Distribution of TA ČR programmes participation TREND THÉTA NCK ÉTA PROSTŘEDÍ PRO ŽIVOT DOPRAVA 2020+ ZÉTA Other DELTA GAMA KAPPA Total Participants 115 44 40 38 33 30 21 19 15 11 3 369 Percentage 31.20% 11.90% 10.80% 10.30% 8.90% 8.10% 5.70% 5.10% 4.10% 3.00% 0.80% 100% The respondents’ organisations’ main R&D focus related to applied research (57.3%) comprises both industrial research (IR) and experimental development (ED). Commercialisation only reached 20%. Basic research represents only 13.4%, which is in line with TA ČR’s focus (see Table 4 ). Table 4 Organisations’ main R&D focus Basic research Applied research (IR/ED) Product commercialization Other Total Participants 50 173 75 4 302 Percentage 16.6% 57.3% 24.8% 1.3% 100% Figure 1 illustrates the primary roles of the 170 organisations within collaborative projects. The most common role is project coordination , followed by that of application partner . Compared with the overall distribution of participants by organisation type, industry actors (SMEs and large enterprises) are more prominently represented as application partners (68.3%) and end-users/commercial partners (90.2%). In contrast, large enterprises are less engaged in research coordination (7.4%) and technology development (6.3%). Research-related organisations dominate in the research coordination function (54.5%), which is consistent with their primary functions. However, SMEs also maintain a notable presence in this role (38.2%), showing a wide engagement in all segments as compared to the large enterprises, which focus more on the application partner, project coordinator , and end-user/commercial partner role. With regard to international collaboration, 71 respondents (35%) reported participating in publicly funded international R&D projects, 14 (7%) were uncertain, and 119 (58%) indicated no engagement in such collaborations. 170 respondents state they have participated in collaborative projects, while 69 have also participated in individual projects without a partner. In addition, 170 respondents state that they track (42.6%) or partially track (40.8%) impact indicators (economic or societal) systematically during or after the project, suggesting a strong governance of the projects. The overall satisfaction of the project on a range of 1 (not satisfied) to 5 (very satisfied) reached a mean score of 3.5 across ten rated categories (Table 5 ). The highest satisfaction was reached in collaboration quality (4.0) and implementation feasibility (4.0), and the least in the administrative process (2.8). Table 5 Satisfaction rating of project parameters Category Mean score Collaboration quality 4.0 Implementation feasibility 4.0 Scientific excellence 3.7 Societal relevance 3.7 Scientific output (scientific articles, methodologies) 3.6 Market potential 3.5 Commercial output (intellctual property, license agreements) 3.3 Flexibility to adapt the project to unforeseen developments 3.3 The administrative process 2.8 The support in commercialization activities 3.1 4.2 Perceived Effectiveness The respondents who had participated in collaborative projects were asked to rate the effectiveness of collaborative projects based on several categories on a scale of 1 (ineffective) to 5 (effective). Figure 2 shows that establishing new business units , such as spin-offs or start-ups, was rated highly ineffective (mean: 1.73), with over half (54%) of respondents rating it as category 1 (least effective). This suggests that establishing new business units is not a primary goal or measure of success in publicly funded collaborative R&D projects. Establishing long-term partnerships (mean: 4.1) and achieving technological innovation (mean: 4.0) were rated as most effective, followed by achieving scientific results (mean: 3.8). Reaching commercialization achieved a mean score of 3.2. Given the clear evidence of inefficiency in establishing new business units, this limitation was examined in greater depth. An evaluation by organisation type took place (Table 6 ), reflecting consistently low ratings across all groups. Research organisations and universities (mean: 1.88) and research institutes (mean: 2.00) rated the criterion slightly higher than businesses. Nevertheless, the ratings remain low, pointing to persistent challenges in commercializing research results. SMEs (mean: 1.81) provided a comparable rating, likely reflecting their limited resources and experience in establishing new entities. Large enterprises, however, assessed this aspect most critically (mean: 1.48), which can be attributed to their more rigid organisational structures, slower decision-making processes, and reduced flexibility in setting up new business units. Table 6 Rating distribution of establishing new business units Establishing new Business Units (Rating scale) Research Organisation - University Research Institute SME Large Enterprise 1 24 14 42 19 2 11 3 15 6 3 10 9 11 4 4 4 1 4 0 5 0 1 3 0 4.3. Barriers in Publicly Funded R&D Projects The assessment of barriers across collaborative projects and individual projects reveals several general trends, as well as distinct differences in how obstacles are perceived. The rating results by category and project type underlie an analysis as shown in Table 7 . On a scale of 1 (not affected) to 5 (affected significantly), respondents rated barriers for collaborative (170 responses) and individual (69 responses) projects. To examine whether the perceived barriers of the eleven categories differ significantly between collaborative and individual R&D projects, we applied an independent-samples and a two-tailed Welch t-test with unequal variance. The results measure how far apart the means are, relative to the variation within groups. Table 7 Barriers of collaborative projects and of collaborative projects calculated as the mean based on the ratings and number of respondents per category. Category N_CPs N_IPs Mean_CPs Mean_IPs Distance p-value Significant (p < 0.05) Cohen's d Effect size interpretation Misalignment of goals and priorities 170 69 1.86 1.51 -0.36 0.010 TRUE 0.36 SMALL Misalignment of technical abilities 170 69 1.81 1.55 -0.26 0.045 TRUE 0.28 SMALL Intellectual property negotiations 170 69 2.10 1.55 0.55 0.001 TRUE 0.49 SMALL Administrative burden and bureaucracy 170 69 3.06 2.93 -0.13 0.458 FALSE 0.11 NEGLIGIBLE Timeline mismatches 170 69 2.25 2.07 -0.17 0.260 FALSE 0.16 NEGLIGIBLE Regulatory compliance 170 69 1.82 1.84 0.02 0.878 FALSE -0.02 NEGLIGIBLE Communication 170 69 1.79 1.72 -0.06 0.637 FALSE 0.07 NEGLIGIBLE Differences in organisational culture 170 69 1.96 1.55 -0.41 0.004 TRUE 0.40 SMALL Funding instability 170 69 1.79 1.70 -0.09 0.525 FALSE 0.09 NEGLIGIBLE Inability to adapt the project to changing market needs 170 69 1.98 1.78 -0.19 0.203 FALSE 0.18 NEGLIGIBLE Project management 170 69 1.83 1.67 -0.16 0.227 FALSE 0.17 NEGLIGIBLE The most pronounced difference was observed in intellectual property negotiations and agreements (p = 0.001, d = 0.55, small effect), where the involvement of multiple independent organisations makes negotiations over licenses, patents, and copyrights more complex, but only with a small effect. Similarly, differences in organisational culture were significantly more pronounced in collaborative projects (p = 0.004, d = 0.41, small effect), reflecting a small level of frictions that arise from integrating diverse working styles, decision-making processes, and priorities across academic and commercial sectors. A statistically significant difference was also identified for misalignment of goals and priorities (p = 0.010, d = 0.35, small effect), suggesting that maintaining alignment of objectives is somewhat more difficult in multi-partner projects. Finally, a significant difference was found for misalignment of technical capabilities (p = 0.045, d = 0.28, small effect), highlighting the challenges of coordinating heterogeneous technical infrastructures and expertise within collaborative environments. ​​In contrast, no significant differences were found between collaborative projects and individual projects in relation to i nability to adapt the project to changing market needs (p = 0.203, d = 0.18, negligible effect), project management (p = 0.227, d = 0.17, negligible effect), timeline mismatches (p = 0.260, d = 0,16, negligible effect), a dministrative burden and bureaucracy (p = 0.458, d = 0.11, negligible effect), funding instability (p = 0.525, d = 0.09, negligible effect), communication (p = 0.637, d = 0.07, negligible effect), or regulatory compliance (p = 0.878, d = -0.02, negligible effect). Taken together, these results suggest that collaborative projects are challenged by a small degree more in the categories of intellectual property, goal and priority alignment, technical ability alignment, and organisational culture, whereas other barriers are experienced similarly across project types. At the same time, some systemic issues stand out across both contexts. Administrative burden and bureaucracy were identified as the most significant barriers in both collaborative projects (mean: 3.06) and individual projects (mean: 2.93), followed by timeline mismatches (collaborative projects: mean: 2.25; individual projects: mean: 2.07). These findings underline the common challenge of procedural rigidity and misalignment between project needs and funding requirements. In the context of individual projects, several additional categories of barriers were asked to be rated, which reflect specific challenges faced by this project type. Limited expertise or skills (mean: 1.67) and reduced innovation potential (mean: 1.70) indicate a low level of concern among respondents. Barriers such as limited external validation or networking (mean: 1.97) and restricted access to resources or infrastructure (mean: 2.00) were rated slightly higher. These factors highlight a level of structural and support limitations impacting individual projects. The most significant barrier within this category was lower funding opportunities (mean: 2.26), underscoring a medium concern of financial constraints as a critical issue for individual project success. A follow-up question was raised as an open-ended prompt about the specific barriers to the commercialization of the respondents’ project outcomes. An analysis of the answers resulted in the following highlights. The barriers to commercialization reported in the dataset cluster around five main dimensions: financial/resource constraints, regulatory and administrative hurdles, market/customer dynamics, organisational gaps, and technical/development limitations. A dominant concern is the lack of funding and resources for the final stages of development, such as prototyping, certification, and scaling, which prevents outputs from reaching market readiness. This is compounded by bureaucracy, slow certification processes, and legislative uncertainty, especially in regulated sectors such as healthcare, energy, and construction. Respondents repeatedly stressed that administrative burdens, both at the national and EU level, create inefficiencies and discourage firms, especially SMEs, from pursuing commercialization. At the same time, market-related obstacles were strongly emphasized. Conservative customers, competition from low-cost providers, and instability caused by global crises reduce willingness to adopt innovative products. Many organisations admitted internal challenges such as weak commercialization orientation, lack of marketing capacity, or misalignment between academia and industry. Finally, technical barriers emerge because research projects often stop at the prototype stage, leaving a gap between R&D results and commercial products. Together, these findings highlight that commercialization is constrained not by a single factor but by an interplay of systemic financial, institutional, market, and organisational barriers that require more targeted support mechanisms from funding agencies and policymakers. 4.4. Aspects Impacting Projects A further comparative evaluation of collaborative projects and individual projects was carried out, assessing the views of collaborative (170 responses) and individual (69 responses) project types on specific aspects of R&D projects. Again, on a scale of 1 = negative (low/slow/complex/unclear) to 5 = positive (high/fast/easy/clear), eleven aspects were rated, and an analysis following independent-samples with a two-tailed Welch t-test with unequal variance was applied (See Table 8 ). Looking at a negative effect, the three lowest collaborative means were observed for management and administration (mean: 3.34), expanded funding opportunities (mean: 3.45), and flexibility in project scope (mean: 3.52). The lowest individual project means were recorded for expanded funding opportunities (mean:2.80), New long-term partnerships (mean:3.04), and Shared resources and infrastructure (mean:3.42), indicating different priorities of the project types. Similarly, looking at the positive effects of the aspects, the highest mean ratings were found for New long-term partnerships (M = 4.03), access to complementary expertise and skills (M = 3.95), and Increased innovation potential (mean: 3.89), while the lowest IP means were recorded for expanded funding opportunities (mean: 2.80), new long-term partnerships (mean: 3.04), and Shared resources and infrastructure (mean: 3.42). The Welch independent-samples t -test revealed statistically significant differences in 10 of the 11 categories examined. IPs were rated significantly higher than CPs in Control over project direction ( p < 0.001), Decision-making processes ( p < 0.001), Management and administration ( p = 0.001), Ownership of results and IP ( p = 0.002), and Flexibility in project scope ( p = 0.023). Conversely, CPs were rated significantly higher than IPs in Shared resources and infrastructure ( p = 0.041), Increased innovation potential ( p = 0.040), Expanded funding opportunities ( p < 0.001), Enhanced reputation and networking ( p < 0.001), and New long-term partnerships ( p < 0.001). Only Access to complementary expertise and skills did not show a statistically significant difference between the two groups ( p = 0.315). Effect sizes (Cohen’s d ) provide further insight into the magnitude of these differences. Medium effects were found for Control over project direction (d = − 0.583) and Decision-making processes (d = − 0.592), both favouring IPs, as well as for Expanded funding opportunities (d = 0.551) and Enhanced reputation and networking (d = 0.589), both favouring CPs. A large effect was identified for New long-term partnerships (d = 0.871), again favouring CPs. Small but significant effects were observed for Management and administration (d = − 0.487), Ownership of results and IP (d = − 0.455), Flexibility in project scope (d = − 0.356), Shared resources and infrastructure (d = 0.345), and Increased innovation potential (d = 0.316). Only Access to complementary expertise and skills produced a negligible effect (d = 0.163), consistent with its lack of statistical significance. Table 8 Mean score of aspects of collaborative projects and of individual projects Category N_CPs N_IPs Mean_CPs Mean_IPs Distance p-value Significant (p < 0.05) Cohen's d Effect size interpretation Control over project direction 170 69 3.88 4.39 -0.51 0.000 TRUE -0.583 MEDIUM Decision-making process] 170 69 3.76 4.30 -0.54 0.000 TRUE -0.592 MEDIUM Management and administration 170 69 3.34 3.84 -0.51 0.001 TRUE -0.487 SMALL Ownership of results and IP 170 69 3.63 4.16 -0.53 0.002 TRUE -0.455 SMALL Flexibility in change project scope 170 69 3.52 3.90 -0.38 0.023 TRUE -0.356 SMALL Access to complementary expertise and skills 170 69 3.95 3.80 0.16 0.315 FALSE 0.163 NEGLIGIBLE Shared resources and infrastructure 170 69 3.77 3.42 0.35 0.041 TRUE 0.345 SMALL Increased innovation potential 170 69 3.89 3.58 0.31 0.040 TRUE 0.316 SMALL Expanded funding opportunities 170 69 3.45 2.80 0.65 0.000 TRUE 0.551 MEDIUM Enhanced reputation and networking 170 69 3.85 3.23 0.62 0.000 TRUE 0.589 MEDIUM New long-term partnerships 170 69 4.03 3.04 0.99 0.000 TRUE 0.871 LARGE The findings demonstrate a clear distinction between collaborative and individual R&D projects, reflecting a balance between governance-related challenges and external benefits. Individual projects were consistently rated higher in areas related to autonomy and control, particularly Control over project direction , Decision-making processes , and Ownership of results and IP . These differences, supported by medium effect sizes, indicate that participants in individual projects experience greater independence and clarity in steering the project and determining its outputs. The higher rating of Management and administration among IPs, despite their smaller scale, suggests that administrative processes are perceived as less burdensome in collaborative settings, though the significant difference indicates that organisational structures may still be viewed differently across project types. Conversely, collaborative projects were rated significantly higher in categories that capture external advantages and long-term strategic benefits. These included Expanded funding opportunities , Enhanced reputation and networking , and especially New long-term partnerships , which showed a large effect size and the most pronounced distinction between project types. These findings confirm that collaborative R&D projects provide a platform for resource pooling, visibility, and enduring institutional connections that are less accessible to individual projects. The higher scores for Shared resources and infrastructure and Increased innovation potential further reinforce this interpretation, even though their effect sizes were small. Interestingly, Access to complementary expertise and skills did not differ significantly between project types, with both collaborative and individual projects assigning high ratings to this category. This suggests that access to specialized knowledge is widely valued across all R&D settings and may not be exclusive to collaborative environments. Individual projects may achieve such expertise through alternative mechanisms such as contracting or partnerships short of full-scale collaboration. Overall, the results point to a governance–benefit trade-off. While collaborative projects impose additional complexity and reduce autonomy, they offer significant advantages in terms of reputation, funding, and the creation of durable networks. These differences are not only statistically significant but also practically meaningful, as indicated by the medium-to-large effect sizes in several categories. From a policy and management perspective, this suggests that maximizing the value of collaboration requires measures to mitigate administrative burdens and decision-making challenges, thereby allowing projects to capitalize on the substantial external benefits that collaboration offers. 4.5 Economic Impact The results (Table 9 ) demonstrate that publicly funded R&D projects generate a range of economic outcomes, though their frequency and distribution differ strongly across organisation types. The most dominant impact across the dataset is the launch of new products or services , reported 154 times (76% of all responses). This underlines the primary role of such projects in fostering product development and innovation. The second most frequent outcome is expected economic impact within two years (105 responses, 52%), showing strong potential for near-term returns. Other notable impacts include jobs created (61 responses, 30%) and patent applications filed (64 responses, 32%), indicating contributions to both market growth and intellectual property generation. Exploring the economic impact by organisation type, the following highlights are found: Research Organisations - Universities show their strongest impact in terms of total number in the areas of new products/services launched (34; 22%), patent applications (19; 30%). They also report meaningful shares in expected economic impact within two years (23; 22%) and jobs created (14; 23%). This reflects their dual role of advancing innovation while transferring knowledge to industry via patents and licenses. Research Institutes show similar but slightly lower results than Research Organisations - Universities, with 20 new products/services launched (13%), 13 patents filed (20%), and 18 expected impacts within two years (17%). Their contributions are primarily focused on knowledge generation and applied research, serving as a bridge between academia and industry. SMEs consistently dominate commercialization-related categories: new products/services launched (49.0%), market expansion (60.0%), jobs created (45.9%), and cost savings/process efficiency (49.3%). They also account for half of those expecting economic impact within two years (49.5%). This positions SMEs as the most agile and market-oriented actors, leveraging public R&D to deliver tangible, near-term economic effects. A weakness is seen in the license contracts concluded (25.0%), likely because they exploit their own research results for competitive advantages instead of transferring them to others. Large Enterprises report more modest contributions, e.g., 25 new products/services launched (16%) and 12 expected impacts (11%). Their role is less prominent in jobs created (7; 11%) and market expansion (7; 14%), suggesting that large enterprises benefit less directly in measurable short-term outcomes from public R&D. They nevertheless maintain relevance in patents (9; 14%), reflecting integration into incremental innovation pipelines. Weak areas across all organisation types include private investment attraction (9 responses, 4%), with SMEs (44%) and universities (33%) contributing the most. Furthermore, start-up/spin-off creation is also limited (11 responses, 5%), led mainly by universities (45%) and SMEs (36%), while large enterprises report none. No economic impact expected was noted in 29 cases (14%), concentrated among universities (38%) and SMEs (34%), suggesting that not all projects translate into measurable economic benefits in the short term or at all. Table 9 Expected economic impact Economic Impact Distribution of participating organisation in number of participants Distribution of participating organisation in % Participants 59 31 94 33 26.6% 14.0% 42.3% 14.9% Research Organisation - University Research Institute SME Large Enterprise Total count Research Organisation - University Research Institute SME Large Enterprise Total % Cost savings/process efficiency achieved 16 8 33 10 67 23.9% 11.9% 49.3% 14.9% 33.0% Economic impact to be expected within 2 years 23 18 52 12 105 21.9% 17.1% 49.5% 11.4% 51.7% Jobs created 14 12 28 7 61 23.0% 19.7% 45.9% 11.5% 30.0% License contracts concluded 14 8 8 2 32 43.8% 25.0% 25.0% 6.3% 15.8% Market expansion 8 5 30 7 50 16.0% 10.0% 60.0% 14.0% 24.6% New products/ services launched 34 20 75 25 154 22.1% 13.0% 48.7% 16.2% 75.9% No economic impact expected 11 5 10 3 29 37.9% 17.2% 34.5% 10.3% 14.3% Notable revenue increase 11 7 25 6 49 22.4% 14.3% 51.0% 12.2% 24.1% Patent applications filed 19 13 23 9 64 29.7% 20.3% 35.9% 14.1% 31.5% Private investment attracted 3 1 4 1 9 33.3% 11.1% 44.4% 11.1% 4.4% Start-up/spin-off creation 5 2 4 0 11 45.5% 18.2% 36.4% 0.0% 5.4% Furthermore, respondents reflected on the expected economic impact based on collaboration types: university-industry collaborations (UIC), industry-only, and research-only projects, and on a scale of 1 (no impact) to 5 (significant Impact), as presented in Fig. 3 . The data indicate that UIC projects demonstrate the highest concentration of economic impact within three years of completion, with modal responses at levels 3 and 4. In contrast, industry-only projects exhibit a more even distribution across the scale, with moderate impacts prevailing but fewer instances of significant impact (level 5). Research-only projects predominantly register at the lower to mid-range of the scale (levels 2 and 3), suggesting limited translation into measurable economic outcomes. These findings highlight the comparatively stronger economic performance of collaborative R&D projects, underscoring the value of cross-sectoral partnerships in enhancing the economic relevance of publicly funded research. Finally, participants were asked to reflect on their experience regarding which type of projects they considered most likely to achieve the highest economic impact. The responses revealed a clear preference for collaborative projects, whether with a single partner, multiple partners, or international partners, cited by a total of 64.4% of respondents. Reinforcing this question, the respondents stated that their preferred type of project was also these three types of projects (69.2%) over the other choices, including individual projects and industry or research-only projects. To deepen this finding, both questions were complemented by an open-ended prompt asking respondents to explain their choice. Due to the similarity of the questions, a combined analysis was carried out by developing a structured codebook. Findings reveal that the type of project most likely to achieve economic impact is defined less by its formal structure and more by the quality of its participants, leadership, and focus. One respondent states: “Project success depends on the current project situation, not the general type“ . Respondents are clear that company involvement is indispensable: “The project leader should always be a company with greater market knowledge than research organisations.” “If a company leads, there is greater focus on commercialization outcomes.” These statements indicate that firms bring market orientation, commercialization capacity, and the financial motivation needed to ensure implementation. Research organisations complement this by contributing scientific expertise, analytical capabilities, and infrastructure, but they are rarely able to drive commercialization alone. “Simplicity of the project scheme is important. Many partners make communication too difficult. More partners only for large projects that are beyond the capabilities of 1 or 2 partners” , states another respondent. The optimal configuration is often a small, focused consortium consisting of one research organisation and one or two companies, which strikes the right balance between knowledge creation and practical application. Larger or international projects can extend reach, bring diverse perspectives, and open new markets. Still, they require careful governance to avoid conflicts over intellectual property, divergent interests, and heavy administrative overhead. Beyond structure, respondents stress that project management, goal clarity, and orientation toward higher TRLs are decisive factors for economic success. Projects led by companies tend to ensure stronger market alignment, faster decision-making, and more efficient resource allocation. Applied research with clear commercialization pathways is rated far more impactful than exploratory research without market connections. At the same time, respondents emphasize the need for flexibility and context sensitivity: in some fields, long-term projects of three to four years are essential, while in others, leaner setups are more appropriate. Rigid administrative criteria and bureaucratic evaluation metrics are widely criticized as barriers that undermine potential. Taken together, the findings suggest that economic impact emerges most reliably when programmes foster company-led, application-focused collaborations with research organisations, supported by flexible funding frameworks that accommodate different project contexts while reducing bureaucratic burdens. 4.6. Societal Impact Societal outcomes attributed to the respondents’ projects are illustrated in Fig. 4 . It highlights that the top three societal outcomes are skills development/education (28.2%) and environmental benefits (24.4%), and improved public health/safety (13.9%) are the highest-ranked societal outputs, while the lowest responses include citizen engagement/co-creation (3.1%) and no societal impact expected (3.1%). With the drive for digitalization, it is surprising that digital inclusion , with 8.7% is resulting in a low impact. Participants were also asked to assess which type of project is most likely to achieve a successful societal impact. The results confirmed once again that UICs, whether with one or multiple partners, were perceived as having the most significant impact (53.2%). In contrast, 21.2% of respondents indicated that all project types, whether collaborative projects, individual projects, or industry- and research-only initiatives, have an equal chance of generating societal impact. To deepen this insight, a follow-up question invited respondents to explain their reasoning. The qualitative analysis of these responses led to the development of structured codes, which are summarized below. The analysis of responses reveals that societal impact is closely tied to inclusiveness, diversity, and practical application. Projects involving a broader set of partners, companies, research organisations, public institutions, and in some cases, NGOs or communities, are seen as particularly effective, since they combine different competencies and perspectives while ensuring that outcomes are relevant to wider societal needs. “Collaborative projects where multiple partners work together have the greatest chance” , says one respondent, and another: “Projects where more different spheres are connected bring higher social impact.” International or cross-sectoral collaborations are also valued, as they bring in fresh viewpoints and open access to new societal contexts. Respondents emphasize that applied research with a clear orientation toward public good has the most significant potential to generate tangible societal benefits. Unlike projects driven solely by commercial objectives, initiatives designed to address real-world challenges, strengthen public services, or raise societal awareness are considered more impactful. Effective collaboration dynamics, including mutual respect, trust, and the integration of corporate and social perspectives, are considered essential enablers of this process. As one participant puts it: “Mutual understanding and respect for corporate and social needs lead to impact.” At the same time, many answers are rooted in personal experience, and several respondents acknowledge the difficulty of assessing societal impact, underlining its multidimensional and context-dependent nature, and or admit uncertainty. Overall, societal impact is perceived not only as an outcome of research and innovation but also as a product of the way partnerships are structured and governed. “Connecting a business and a public organisation ensures social relevance.” 5. Discussion This study contributes to the growing body of research on the impact of publicly funded R&D by providing empirical insights from project leaders in the Czech Republic. The findings align with literature that highlights the importance of collaboration as a determinant of impact, particularly within the framework of the Triple and Quadruple Helix models (Etzkowitz & Leydesdorff, 2000 ) (Cunningham & Gök, 2012 ). Addressing the first research question on preferred project structures and perceived potential for economic impact, respondents consistently emphasized that collaborative projects, especially those involving UICs with either one, multiple, or international partners, are perceived as most effective in achieving economic impact. These were considered more likely than individual projects to generate tangible outcomes such as new products, services, and market expansion, while also fostering long-term networking and partnerships. By contrast, individual projects were valued for their autonomy, flexibility, and clarity over intellectual property, but were seen as less capable of generating systemic integration or long-term economic value. This confirms that the composition and structure of consortia play a central role in shaping commercialization potential. Addressing the second research question on economic and societal impacts participants expect from publicly funded R&D projects implies that the role of different organisation types further underlines differentiated contributions within the innovation system. SMEs emerged as the most agile commercialisers of research results, leading in product launches, job creation, and market expansion, whereas universities and research institutes contributed more significantly to patents, licenses, and knowledge-based outputs that underpin future innovation. Large enterprises, by contrast, appear to rely on incremental integration of R&D outcomes, reporting fewer short-term impacts. This division of roles suggests that funding programs should acknowledge the distinct capacities of different actors rather than adopt one-size-fits-all approaches. In terms of societal impacts, the findings highlight that projects involving multiple stakeholders, business, academia, public sector, and civil society, tend to generate greater societal value, especially in education, skills development, environmental sustainability, and public health. However, areas such as digital inclusion and citizen engagement were reported less frequently, suggesting uneven distribution of societal benefits across domains. Notably, respondents stressed that societal impacts often materialize over longer time horizons and depend on effective collaboration, which cannot be captured solely through short-term economic metrics. The third research question, focusing on barriers influencing the realization of economic and societal impacts from publicly funded R&D projects, is addressed through the systemic challenges that continue to constrain the realization of both economic and societal outcomes. Respondents repeatedly pointed to bureaucracy, administrative burden, and inflexible project rules as major obstacles, echoing findings from OECD (OECD, 2019 ) on inefficiencies in R&D governance. Certification delays and regulatory uncertainty, especially in heavily regulated sectors such as healthcare, construction, and energy, were identified as further bottlenecks. The weak performance in spin-off creation and private investment attraction across all organisations highlights structural gaps in translating research into entrepreneurship and mobilizing private capital. At the same time, external shocks, such as the COVID-19 pandemic, geopolitical crises, and energy price volatility, were seen as additional barriers to market uptake, even when technological readiness had been achieved. Enabling factors identified by respondents included well-structured partnerships, strong company involvement, realistic timelines, and support for higher-TRL projects, all of which enhance commercialization prospects and consequently impact. Furthermore, to address societal impact, administrative and financial requirements should be adapted to lower barriers for diverse actors such as NGOs, municipalities, and community organisations, for instance, through lighter reporting or tailored cost models. At the same time, funding calls should explicitly integrate societal impact criteria, assessing how projects contribute to well-being, sustainability, inclusiveness, and social innovation alongside scientific and economic outcomes. Overall, the analysis suggests that funding agencies need to shift from rigid, one-size-fits-all programme designs toward more flexible, performance and impact-oriented schemes. Respondents consistently highlight that economic impact depends less on the formal type of project and more on how well partnerships are structured, managed, and aligned with market needs. This means that funding instruments should prioritize company involvement and leadership, since companies provide the most substantial incentives and capacities for commercialization. At the same time, programmes should create room for small, focused consortia of one research organisation and one or two companies, which are widely viewed as the most effective structure for balancing scientific depth with market orientation. While large consortia and international collaborations can be valuable, they also carry higher risks of coordination problems and knowledge-protection conflicts, necessitating tailored support mechanisms such as mediation, IP-sharing templates, or regulatory guidance. Funding agencies should also emphasize quality, competence, and realistic timelines over rigid formal requirements. Instead of administrative-heavy evaluation metrics (such as quotas or formal indicators not directly tied to outcomes), assessment should focus on clarity of goals, commercialization pathways, and the demonstrated capabilities of partners. Programmes should explicitly support projects at higher technology readiness levels (TRLs), where the likelihood of economic impact is greatest, while still maintaining a pipeline for earlier-stage research. Longer project durations (three to four years in specific fields) should be allowed where needed, avoiding artificial pressure for shorter cycles that cannot deliver meaningful outcomes. Finally, funding frameworks should embed market access, certification, and internationalization pathways, enabling research results to cross into practice more effectively. In short, funding design should become more context-sensitive and impact-driven, ensuring that programme structures actively facilitate, not hinder, the translation of research into tangible economic and societal value. This study highlights several directions for further investigation. Future research should adopt longitudinal approaches to capture medium- and long-term outcomes beyond project completion, as many societal benefits and spillovers emerge only after several years. Comparative analyses across EU countries and regions would also clarify how different innovation systems and governance structures shape funding effectiveness. Additional work is also needed on sector-specific dynamics, particularly in regulated fields such as healthcare, energy, and construction, where certification and market access are significant barriers. In addition, future studies should examine why publicly funded projects yield relatively few spin-offs and start-ups, and how entrepreneurship can be better supported. Finally, improved frameworks for measuring societal impact, stronger integration of non-traditional actors (e.g., NGOs, municipalities), and analysis of global disruptions such as pandemics or war could deepen understanding of how public R&D creates sustainable economic and societal value. This study has several limitations. As a cross-sectional survey, it captures project leaders’ perceptions at a single point in time, which may not fully reflect long-term economic or societal impacts. Reliance on self-reported data introduces subjectivity and possible recall bias, while non-response bias may have led to an overrepresentation of more successful or engaged actors. Moreover, focusing solely on TA ČR-funded projects limits the generalizability of the findings to other funding schemes or national contexts. A further limitation arises from the fact that the survey was addressed to entire organisations rather than to individual project leaders. In the case of larger institutions, such as universities, respondents were often unable to represent data from the organisation as a whole, meaning that some responses may be based on individual estimations or were left unanswered, potentially reducing comparability across organisation types. 6. Conclusion This study has shown that publicly funded R&D projects in the Czech Republic generate both economic and societal impacts, but their effectiveness is strongly conditioned by project structure, organisational roles, and systemic barriers. Collaborative projects, particularly those involving UICs, emerge as the most impactful in terms of economic outcomes and societal relevance, while individual projects provide autonomy and efficiency but limited broader integration. SMEs act as central drivers of commercialization, whereas research organisations, universities, and research institutes, contribute knowledge-based foundations that enable future innovation. The barriers identified, bureaucracy, lack of ambition in new business unit creation, certification delays, funding gaps after project completion, and market conservatism, are systemic issues that undermine the efficiency of public R&D investment. Addressing these requires programme designs that go beyond funding allocation to actively support commercialization pathways, reduce administrative burdens, and embed mechanisms for business unit creation and societal value creation. Funding agencies should prioritize company involvement and applied research, support small but effective consortia, and adapt project durations to sectoral needs. From a policy perspective, the findings suggest that the impact of public R&D can be maximized by designing flexible, context-sensitive, and impact-oriented funding schemes. These should balance economic and societal objectives, incentivize high-quality collaboration, and provide tailored support for commercialization and social uptake. Future research should build on this survey by conducting comparative studies across EU member states and by combining quantitative surveys with in-depth case studies to capture the full spectrum of public R&D outcomes. Declarations Ethical Approval and Consent to Participate All participants provided written informed consent in accordance with the requirements of the Ethics Committee of the corresponding author’s affiliated institution. Consent for Publication No identifiable personal information of the survey participants is included in the manuscript. All authors provide consent for the publication of the manuscript. Funding The authors declare that they have no known competing financial or non-financial interests that could have influenced the work reported in this paper. The work received no funding. Author Contribution U.M-S. and M.K. have equally contributed to the design of the survey and the collection of its results. M.K. has prepared the majority of the computational analysis, whereas U.M-S. has prepared the majority of the literature review and manuscript. Both have complemented each other's work. M.B. and P.K. have jointly supervised and reviewed the work. Acknowledgment The authors gratefully acknowledge the valuable contributions of all survey participants and the support of the colleagues of the Technology Agency of the Czech Republic for this study. The manuscript benefited from AI-assisted proofreading provided by Grammarly, OpenAI’s ChatGPT, which helped refine spelling, grammar, and sentence structure. The final version reflects the authors’ independent revisions. 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1","display":"","copyAsset":false,"role":"figure","size":40664,"visible":true,"origin":"","legend":"\u003cp\u003eOrganisations’ main roles\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7878204/v1/99a1a45a54c6dac9b19af4a4.png"},{"id":95807135,"identity":"58ed8be6-1310-4a67-a3ef-e00543568fac","added_by":"auto","created_at":"2025-11-13 08:48:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":30315,"visible":true,"origin":"","legend":"\u003cp\u003eEffectiveness of collaboration\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7878204/v1/60d5e5a8d50b1834ee98e9b2.png"},{"id":95807005,"identity":"b0da0243-3362-4130-b6b0-f2d9ca21a362","added_by":"auto","created_at":"2025-11-13 08:48:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28846,"visible":true,"origin":"","legend":"\u003cp\u003eExpected economic impact achieved within 3 years after project completion\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7878204/v1/7ca958737c00baeb14f63d68.png"},{"id":95807210,"identity":"cef678ec-47d9-4a94-b2e1-e96c272edaa8","added_by":"auto","created_at":"2025-11-13 08:48:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":88717,"visible":true,"origin":"","legend":"\u003cp\u003eSocietal impact on project outcomes\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7878204/v1/dd35deeacea160df637e0dc3.png"},{"id":107351081,"identity":"e7b69456-054e-4b2c-b862-96fa30795e4f","added_by":"auto","created_at":"2026-04-20 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Introduction","content":"\u003cp\u003ePublic research and development (R\u0026amp;D) funding is considered to play a pivotal role in fostering innovation and driving economic growth across Europe (Dworak et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This strategic role is prominently prioritized by the European Union (EU) (\u003cem\u003ePriorities 2024\u0026ndash;2029 - European Commission\u003c/em\u003e, n.d.), through funding frameworks such as Horizon Europe (\u003cem\u003eHorizon Europe the EU\u0026rsquo;s Funding Programme for Research and Innovation\u003c/em\u003e, n.d.) and the European Innovation Council (EIC) (\u003cem\u003eEurope\u0026rsquo;s Flagship Innovation Programme to Identify, Develop and Scale up Game Changing Innovations\u003c/em\u003e, 2025), which provide substantial, targeted resources to stimulate research and innovation activities across member states. Complementing these EU-wide initiatives, individual EU member countries implement various national and regional funding schemes tailored to their specific innovation ecosystems, priorities, and capabilities, thereby creating a multilevel framework that supports robust R\u0026amp;D activities\u003c/p\u003e\u003cp\u003ePublic funding is accompanied by high expectations for both tangible and intangible economic and societal impacts, leading to a growing interest in comprehensively and systematically evaluating these outcomes. Recent literature converges on the conclusion that both tangible metrics (e.g., patents, commercialization, job creation, GDP contribution) and intangible metrics (e.g., knowledge spillovers, capacity building, skills development, societal well-being) are essential to capture the full spectrum of publicly funded R\u0026amp;D impacts (Ebersberger, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; M. Cohen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Directorate-General for Research and Innovation (European Commission), \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Georgiou et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Almus \u0026amp; Czarnitzki, n.d.). These metrics must account for immediate outputs, medium-term outcomes, and long-term effects, reflecting the complex and dynamic nature of R\u0026amp;D impact pathways. Expectations and metrics reflect a rather linear view of the mechanics behind the formation of new technologies, products, or services put in the market. However, the reality is often non-linear, as there are many sources of ideas, knowledge, and other necessary resources, as well as market demand, which can or cannot exist. In economics and market theory, this is typically expressed as market failures that can be addressed through targeted aid, which may have spillover effects (Dhanora et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) (Nie et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, there is an economic theory by Mazzucato that reflects the role of public support in creating new markets (Yencha, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) (Bagattolli et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This leads to a more complex, holistic view of how to measure the impact of public funding of research and innovation activities. Viewing innovation as a linear sequence, from project initiation through support, results, innovation, and market entry, oversimplifies its complexity and neglects systemic interdependencies. Innovation systems research shows that innovation emerges through iterative, non-linear interactions among diverse actors (Lundvall, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), (Freeman, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), (Edquist, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Beyond statistical or econometric analyses, the use of sociological and psychological methods can reveal fascinating insights.\u003c/p\u003e\u003cp\u003eWithin national contexts, funding agencies often serve as crucial intermediaries, administering public R\u0026amp;D programs aligned with national strategic specializations. In the Czech Republic, for instance, this role is undertaken by the Technology Agency of the Czech Republic (TA ČR), which has been orchestrating a wide range of applied research and innovation funding programs since 2009. TA ČR\u0026rsquo;s operations reflect a systematic approach to evaluation and funding, rooted in long-standing national evaluation methodologies. These methodologies incorporate multi-criteria assessments covering scientific excellence, innovation potential, and societal relevance.\u003c/p\u003e\u003cp\u003eThe objective of the paper is to deepen the understanding of the experiences and perceptions of participants involved in publicly funded R\u0026amp;D projects regarding the projects\u0026rsquo; economic and societal impacts. To this end, the authors conducted a comprehensive survey targeting R\u0026amp;D actors financed by public programs by TA ČR, aiming to illuminate three central research questions:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat types of R\u0026amp;D project structures (e.g., collaborative vs. individual) are preferred by participants in publicly funded R\u0026amp;D projects, and how do these preferences relate to the perceived potential for economic impact?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat are the specific economic and societal impacts that participants expect from publicly funded R\u0026amp;D projects, and how do these expectations vary by organisational type and project characteristics?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat are the main barriers and enabling factors identified by participants that influence the realization of economic and societal impacts from publicly funded R\u0026amp;D projects?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThese questions address crucial gaps in understanding how funding frameworks translate into perceived value and how economic and societal outcomes are envisioned by those directly engaged in project execution.\u003c/p\u003e\u003cp\u003eThe paper follows a classical structure comprising a literature review, a detailed methodology, and a critical analysis of survey results on the impact of R\u0026amp;D funding. The discussion situates findings within broader policy debates, while the conclusion summarizes contributions and offers recommendations for future research and practice.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Market Failure and Innovation\u003c/h2\u003e\u003cp\u003eThe rationale for public R\u0026amp;D funding can be attributed to the market failures that hinder the efficient generation of innovation (Edler et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Bryan and Williams (Bryan \u0026amp; Williams, n.d.) provide a comprehensive review of literature on the economics of innovation, focusing on why markets alone often fail to generate optimal innovative activity and how public policies, such as funding instruments, can address these shortcomings. Key market failures identified include knowledge spillovers, which lead to firms underinvesting in R\u0026amp;D because the returns from inventions cannot be fully captured, and coordination failures that prevent complementary innovations from emerging when individual innovators cannot align their efforts. Furthermore, capital market imperfections restrict financing for high-risk, long-term research, especially for smaller firms. Inefficient intellectual property rights (IPR) regimes can either discourage innovation by granting overly broad rights or fail to protect inventors adequately. Targeted public policy interventions help address these shortcomings and stimulate innovation, for example, through publicly funded research, development, and innovation (R\u0026amp;D\u0026amp;I) initiatives.\u003c/p\u003e\u003cp\u003eMartin and Scott (Martin \u0026amp; Scott, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) also highlight how corresponding market failures (knowledge spillovers, coordination problems, and financing constraints) hinder innovation and justify public intervention. They emphasize that innovation dynamics differ across sectors, requiring tailored policy responses. Effective tools include R\u0026amp;D grants, tax incentives, and support for collaborative networks, each suited to specific industry contexts. The authors argue for a strategic, sector-sensitive approach to innovation policy to better address the underlying causes of underinvestment in technological development. Mazzucato (Mazzucato, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) argues that traditional innovation policy, which focuses on addressing market failures, is too limited to drive inclusive and transformative growth. She proposes a mission-oriented approach, where the state actively shapes and creates markets through strategic investments and coordination. Key issues include the need for directional public policy, dynamic evaluation tools, capable public institutions, and mechanisms to share both risks and rewards of innovation. Mazzucato calls for rethinking the role of government from passive fixer to active driver of innovation and economic change.\u003c/p\u003e\u003cp\u003ePublic R\u0026amp;D funding as a tool to correct market failure not only addresses structural barriers to innovation but also enables high-risk, long-term research that the private sector often avoids. It stimulates collaboration across industries and drives innovation in areas of strategic and societal importance. Its impact lies in shaping the direction of technological development and ensuring that both the risks and rewards of innovation are more broadly shared across society.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Institutions Guiding Innovation Monitoring\u003c/h2\u003e\u003cp\u003eA range of international and national institutions are actively engaged in monitoring innovation, particularly with respect to publicly funded R\u0026amp;D. At the global level, the OECD plays a central role through its widely adopted Frascati (OECD, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Oslo Manuals (OECD \u0026amp; Eurostat, 2018), which set the standard for collecting and analysing R\u0026amp;D and innovation data. The European Commission, notably through DG Research and Innovation and the Joint Research Centre (JRC), has developed comprehensive tools like the Innovation Scoreboard, Key Impact Pathways under Horizon Europe, and Smart Specialisation Strategies (S3) to assess regional and sectoral innovation performance across the EU. In developing countries, the UNESCO Institute for Statistics supports the collection of R\u0026amp;D data to enhance comparability and inform policy capacity. The World Bank complements this by providing Science, Technology, and Innovation (STI) indicators and toolkits tailored to institutional diagnostics and policy design in emerging economies. National-level monitoring is carried out by statistical offices and innovation agencies, which produce detailed evaluation reports aligned with both domestic priorities and international standards. These institutions collectively enable evidence-based innovation policy through standardized metrics, dynamic assessment frameworks, and region-specific insights. As summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, several national and global institutions have proposed guidelines for designing and monitoring innovation.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNational and International Institutions guiding innovation monitoring.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInstitution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrameworks\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFocus\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUse/Application\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNational Innovation Agencies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eImpact evaluation reports\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCountry-specific innovation and public funding measurement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAdapted to national policy priorities\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNational and European Statistical Offices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNational R\u0026amp;D statistics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCountry-specific statistics connected to Eurostat (\u003cem\u003eHome - Eurostat\u003c/em\u003e, n.d.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLinked with European standards\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEuropean Commission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHorizon Europe KIPs (\u003cem\u003eRESEARCH AND INNOVATION - Horizon Europe Monitoring and Evaluation Framework\u003c/em\u003e, n.d.), (European Commission. Directorate General for Research and Innovation. et al., 2022),\u003c/p\u003e\u003cp\u003eInnovation Scoreboard (Reid \u0026amp; Markianidou, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e),\u003c/p\u003e\u003cp\u003eRIS3 (Directorate-General for Regional and Urban Policy (European Commission) et al., 2012)20/10/2025 14:25:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eImpact assessment of EU R\u0026amp;I programmes and regional innovation strategies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMonitors Horizon Europe, cohesion policy, smart specialisation strategies\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOECD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrascati Manual (OECD, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e),\u003c/p\u003e\u003cp\u003eOslo Manual (OECD \u0026amp; Eurostat, 2018)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandardized methodologies for R\u0026amp;D and innovation data collection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUsed globally for measuring public R\u0026amp;D investment, innovation inputs and outputs\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUNESCO Institute for Statistics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGlobal R\u0026amp;D Statistics (based on Frascati Manual) (UNESCO Institute for Statistics, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInnovation and R\u0026amp;D metrics in developing countries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSupports international comparisons and policy capacity building\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorld Bank\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePractitioner\u0026rsquo;s Guide to Innovation Policy (Frias et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e),\u003c/p\u003e\u003cp\u003eSTI indicators (Watkins \u0026amp; Ehst, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePolicy making in developing countries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUsed for policy design, institutional diagnostics, capacity development\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Challenges in Measuring Innovation Impact\u003c/h2\u003e\u003cp\u003eA major challenge in studying the outcomes of publicly funded initiatives lies in accurately measuring their processes and impacts on innovation. Bryan and Williams (Bryan \u0026amp; Williams, n.d.) emphasise that capturing knowledge spillovers is challenging, as much of the diffusion occurs informally and is not well-documented in data sources such as patents or citations. Also, mapping the timing and extent of diffusion across industries and geographies is complex, with long lags and inconsistent observability. Standard metrics such as R\u0026amp;D spending or patent counts are criticized for failing to reflect the true quality or economic value of innovations. Linking specific innovations to broader economic outcomes like productivity growth or inequality is methodologically challenging, due to issues like endogeneity and unobserved variables, which complicate causal inference.\u003c/p\u003e\u003cp\u003eAttribution is complex, as innovation outcomes typically emerge from the interplay of multiple actors and factors, making it difficult to isolate the effects of a specific intervention (Gault, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Gault states that the long-time lags between investment and observable results hinder timely assessment. At the same time, many outputs, such as knowledge creation or institutional change, are intangible and hard to quantify. Innovation processes are also dynamic and nonlinear, meaning traditional linear evaluation models often fall short. Standard measurement tools, like patent counts or R\u0026amp;D expenditure, may introduce biases by overlooking broader societal or environmental impacts and underrepresenting less technology-intensive sectors. Furthermore, the highly context-dependent nature of innovation and the frequent absence of reliable counterfactuals further complicate efforts to draw robust conclusions about impact.\u003c/p\u003e\u003cp\u003eCohen et al. (M. Cohen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) identify four key methodological challenges in measuring the impact of university-industry collaborations (UICs). First, the multidimensional nature of impact, ranging from tangible to intangible, from short-term to long-term, and from direct to indirect effects, makes consistent measurement complex. Second, there is a causal attribution challenge, as it is difficult to determine whether observed outcomes (e.g., increased sales) result from the UIC itself or other external factors. Third, the identification of impacts is hindered by subjective perceptions and differing expectations across individuals and institutions, especially regarding future societal benefits. Lastly, data limitations pose a significant barrier due to insufficient or inconsistent data collection practices, low response rates, and contextual factors (e.g., geographical or institutional) that limit data availability and reliability. Together, these challenges complicate the evaluation and understanding of UIC outcomes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Evaluation Methods for R\u0026amp;D Impact\u003c/h2\u003e\u003cp\u003eDespite the complexity and diversity of challenges in measuring innovation impact, funding agencies must implement metrics to obtain feedback on their investments, meet stakeholder requirements, and improve programme design. Scientific literature and EU policy documents explore and propose a number of evaluation methods. For example, Choen et al. (M. Cohen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) identified six main categories of impact resulting from university\u0026ndash;industry R\u0026amp;D collaborations: intellectual (e.g., knowledge creation, learning), economic (e.g., wealth generation, funding), technological (e.g., new technologies, patents), environmental (e.g., pollution reduction), social (e.g., regional development, job creation), and strategic (e.g., reputation, future collaborations). These impacts operate at micro (individual), mezzo (organisational), and macro (societal) levels and can be tangible or intangible, direct or indirect, as well as short- or long-term.\u003c/p\u003e\u003cp\u003eEuropean Commission\u0026rsquo;s Expert Group report (Directorate-General for Research and Innovation (European Commission), \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) on knowledge transfer metrics aims to harmonize and improve the comparability of data regarding knowledge transfer activities from public research organisations (PROs) to business and society across Europe. The group identifies a set of core indicators for regular monitoring: research agreements, invention disclosures, patent applications and grants, executed licenses, licensing income, and spin-offs. Supplementary metrics, such as knowledge transfer with small and medium-sized enterprises (SME), regional engagement, and patent utilization, are also recommended for more detailed monitoring. Performance indicators should be normalized by research expenditure or personnel for robust comparison. While the current focus is on patenting and licensing, reflecting the activities of the Knowledge and Technology Transfer Office (KTTO), the report acknowledges the need to capture broader knowledge transfer mechanisms in the future. Benoit et al. (Directorate-General for Research and Innovation (European Commission) et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) suggest a knowledge complexity approach to complement traditional metrics. The complexity framework analyses the structure and relationships within innovation ecosystems, helping policymakers understand technological capacity, diversification potential, and strategic development opportunities.\u003c/p\u003e\u003cp\u003eRoper et al. (Roper et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) investigate the ex-ante and ex-post evaluation methods, two distinct approaches used to assess the impact of publicly funded R\u0026amp;D projects. Ex-ante evaluations are conducted before the implementation of a project or investment and aim to forecast its potential outcomes, benefits, and risks. This forward-looking assessment helps decision-makers determine whether to proceed with funding and guides resource allocation by posing \u003cem\u003e\"what-if\"\u003c/em\u003e scenarios. It relies heavily on secondary data and predictive models to estimate expected impacts and benefits. In contrast, ex-post evaluations take place after project completion, focusing on measuring and analysing the actual outcomes, effectiveness, and impacts of the funded research. These retrospective assessments use primary data collected during or after the project, providing evidence of realized benefits, lessons learned, and policy effectiveness. While ex-ante evaluations are preventive and guide strategic planning, ex-post evaluations validate results and improve future project design by reflecting on real-world achievements and shortcomings. Spaapen \u0026amp; van Drooge\u0026rsquo;s (Spaapen \u0026amp; Van Drooge, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) framework thus offers a dynamic, process-oriented lens, shifting away from rigid before\u0026ndash;and\u0026ndash;after dualities, and toward recognizing interaction patterns that meaningfully contribute to impact, whether evaluated prospectively (ex-ante) or retrospectively (ex-post). Petrin (Petrin, n.d.) concludes that pure econometric evaluations of government R\u0026amp;D support should be complemented by long-term ex-post studies and in-depth qualitative case analyses to capture both measurable results and contextual insights.\u003c/p\u003e\u003cp\u003eQualitative versus quantitative methods further offer approaches to evaluating R\u0026amp;D impacts, providing complementary insights (Georgiadis et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Quantitative methods use numerical data and statistical analysis to measure inputs, outputs, and outcomes of R\u0026amp;D objectively. Common examples include bibliometric analysis (e.g., citations, patents), cost-benefit analysis, econometric modelling, and commercialization metrics. These approaches provide clear, comparable indicators of performance and economic impact. Qualitative methods, in contrast, focus on understanding the context, processes, and subjective aspects of research impact that numbers alone may miss. Techniques such as case studies, interviews, expert panels, and narrative analysis explore the added value, effectiveness, and unexpected outcomes of R\u0026amp;D investments. Increasingly, combining qualitative insights with quantitative data (mixed method) is recommended for a well-rounded assessment of publicly funded research impact.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Key Factors Influencing Impact\u003c/h2\u003e\u003cp\u003eOne key factor influencing the impact of publicly funded R\u0026amp;D projects is the role of collaboration and networks, conceptualized in the Triple Helix model by Etzkowitz and Leydesdorff (Etzkowitz \u0026amp; Leydesdorff, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). This model highlights the dynamic interaction between universities, industry, and government as essential for innovation and knowledge exchange. Collaboration increases the innovation output and effectiveness of public funding by fostering the sharing of resources, knowledge, and capabilities among partners, thus enhancing the overall impact. Empirical studies have shown that collaboration combined with public funding leads to higher innovation output, especially when collaborative innovation activities are actively supported within funding schemes (Ebersberger, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) (Cunningham \u0026amp; G\u0026ouml;k, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) (Czarnitzki et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) (Hottenrott \u0026amp; Lopes-Bento, 2014). Networks help overcome financial constraints and stimulate private sector R\u0026amp;D investment, particularly benefiting smaller firms and SMEs by leveraging additional resources and facilitating access to markets and expertise (Hilmersson \u0026amp; Hilmersson, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAnother critical influence is the absorptive capacity of organisations, a concept developed by Cohen and Levinthal (W. M. Cohen \u0026amp; Levinthal, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), which refers to the ability of firms or institutions to recognize, assimilate, and apply external knowledge. Effective use of publicly funded R\u0026amp;D outputs depends significantly on this capacity, enabling the translation of research findings into innovative products, processes, or services.\u003c/p\u003e\u003cp\u003eGovernance and project management are vital in publicly funded R\u0026amp;D projects to ensure alignment of objectives, efficient resource allocation, and coordination through the project lifecycle. Flexible governance impacts partner integration, project costs, and quality (Pisano, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Strong governance and minimizing bureaucracy improve performance and motivation, especially in novel collaborations (Cunningham \u0026amp; G\u0026ouml;k, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Adaptive governance models help consortia and funding agencies achieve better innovation outcomes by evolving through project phases (Kim et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Well-designed funding instruments and institutional settings play a crucial role in shaping outcomes by promoting cooperation, flexibility, and responsiveness to specific needs. European University Alliances, for instance, implement shared management structures and joint governance bodies that foster sustainable collaboration across universities, pooling resources and aligning strategic goals effectively (Estermann \u0026amp; Pruvot, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Regional innovation policies in Europe emphasize place-based approaches that tailor UIC frameworks to local institutional contexts, enhancing the exploitation of R\u0026amp;D results for regional competitiveness (Morisson \u0026amp; Pattinson, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, OECD reports highlight a shift towards hybrid governance models combining formal institutional arrangements with relational trust-building mechanisms to strengthen university-industry innovation partnerships across Europe (OECD, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Gaps in Existing Literature\u003c/h2\u003e\u003cp\u003eExisting literature on the impact of publicly funded R\u0026amp;D projects reveals several important gaps. First, there is a lack of studies that capture the perspectives of different stakeholders involved, which limits our understanding of the diverse impacts and challenges. Second, soft impacts such as organisational learning, trust-building, and stakeholder engagement remain underrepresented despite their significant role in sustaining innovation processes. Finally, there is a shortage of comparative studies across different disciplines and sectors, which restricts insights into how funding effectiveness and impact mechanisms vary in various research contexts. Addressing these gaps is crucial to developing a more nuanced and comprehensive evaluation framework for public R\u0026amp;D investments.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThe applied methodology follows a Survey Study Method Plan (SSMP) as outlined by Creswell and Creswell (Creswell \u0026amp; Creswell, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and is presented in detail in the subsequent section.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Survey Design\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe purpose of the survey is to collect and evaluate empirical data on the impact of publicly funded R\u0026amp;D projects, based on a representative sample of project participants, in particular, project leaders. Quantitative research in the form of a survey study was selected due to its greater capacity to capture the breadth and diversity of collaboration preferences, barriers, and outcomes. The survey consisted of an introductory section explaining the purpose of the study, followed by four main sections, each comprising multiple items designed to capture different aspects of the project experience and impact. These included:\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eGeneral information: Demographic questions aimed at identifying the institutional affiliation, organisational size, and role of the respondent (5 questions)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eProject structure: Indication of the project type they had been involved in (1 question), followed by targeted questions depending on whether the project was collaborative (6 questions) or non-collaborative (3 questions\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eImpact of funding: Assessed perceived outcomes and included economic impact (7 questions), societal impact (3 questions), and general impact (2 questions)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAdditional perspectives: Questions were included to capture broader reflections and suggestions for improvement (5 questions).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eQuestions were designed using primarily multiple-choice and Likert-type scale questions (Matas, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), but also some open-ended questions. Most questions permitted multiple responses. The questionnaire was conducted cross-sectionally, specifically capturing data from participants at one point in time (Creswell \u0026amp; Creswell, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Participants could choose between the Czech and English languages to accommodate an inclusive target audience. Participation in the survey was voluntary and anonymous. No personal data was collected, and informed consent was obtained at the beginning of the questionnaire. The study adhered to general ethical standards for social science research. The definitions of the organisation types used in this study are based on the Commission Regulation (EU) No 651/2014 (\u003cem\u003eRegulation \u0026minus;\u0026thinsp;651/2014 - EN - General Block Exemption Regulation - EUR-Lex\u003c/em\u003e, n.d.).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Population and Sample\u003c/h2\u003e\u003cp\u003eThe sampling frame (Groves et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) for this study was defined as participants involved in R\u0026amp;D projects funded by the TA ČR. These individuals represented organisations, including academic institutions, research institutes, SMEs, and large corporations. The eligible population was involved in projects concluded between 2022 and 2023 that had entered the phase of implementing project results. Projects involving the target population were funded by one or more of the following TA ČR programmes: TREND, DOPRAVA 2020+, PROSTŘED\u0026Iacute; PRO ŽIVOT, TH\u0026Eacute;TA, \u0026Eacute;TA, NCK, DELTA, GAMA, and Z\u0026Eacute;TA.\u003c/p\u003e\u003cp\u003eBased on TA ČR\u0026rsquo;s records, the overall population of individuals involved in these funded R\u0026amp;D projects was identified as 13,667. From this population, a group of 2,065 registered project leaders was identified. This target group was considered most appropriate for survey distribution, as project leaders typically have a comprehensive understanding of the project\u0026rsquo;s goals, execution, and outcomes and could provide a comprehensive view. Stratification in terms of represented organisations took place. It was important that all relevant organisations were represented in this survey, and bias was reduced. Of this project leader group, 1,952 persons received the invitation, making it the final target sample. The remaining contacts (113) were excluded due to invalid contact information. In total, 205 respondents took part, of which two were disqualified, either because the survey was insufficiently completed or the respondent indicated that their organisation had not received public funding, a prerequisite for the survey. This resulted in a final dataset of 203 valid responses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Instrumentation\u003c/h2\u003e\u003cp\u003eThe survey was developed in and shared through LimeSurvey (\u003cem\u003eLimeSurvey \u0026mdash; Free Online Survey Tool\u003c/em\u003e, n.d.), an online tool frequently used by TA ČR. Dissemination took place via targeted email, allowing for an economic, timely, and standardized distribution both for the participants and the researchers (Lim, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Participants could conveniently complete the questionnaire on desktops, tablets, or mobile devices, while researchers could extract reliable raw data in the form of Excel tables and a LimeSurvey-generated analysis. The survey was pilot tested among researchers and members of TA ČR to ensure the correct spelling, readability, and flow of the questions, as well as the technical precision of the survey. The survey responses were monitored regularly to ensure an adequate response rate. A follow-up reminder email was sent two weeks after the initial distribution to encourage additional participation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Variables of the Study\u003c/h2\u003e\u003cp\u003eThis study examines the impact of publicly funded R\u0026amp;D projects by analysing responses from project leaders involved in TA ČR-funded initiatives. The variables were structured into independent, dependent, and control categories to support a clear analytical framework (Creswell \u0026amp; Creswell, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Independent variables include project characteristics such as the type of project (collaborative or non-collaborative), organisational affiliation (e.g., academia, research institutes, SMEs, or large corporations).\u003c/p\u003e\u003cp\u003eThe dependent variables focus on the impact of the projects and are grouped into three domains: economic, societal, and general impact. Economic impact includes indicators such as product or service development, market expansion, and job creation. Societal impact refers to contributions to public welfare, sustainability, or skills creation. General impact captures broader outcomes, including strategic alignment and overall satisfaction with the public funding received. All impact variables were measured using Likert-scale items or multiple-choice and open-ended questions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Data Analysis\u003c/h2\u003e\u003cp\u003eThe survey was open for four weeks and generated 205 responses, of which 203 met the criteria for completeness and eligibility and were included in the analysis. A quantitative analysis method was employed, with the collected data examined using both descriptive and inferential statistical techniques to identify patterns, correlations, and group-specific differences (Creswell \u0026amp; Creswell, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Descriptive statistics allowed for summarising central tendencies and distributions, while inferential techniques enabled testing for group differences and relationships between variables, ensuring that the findings are not only illustrative but also statistically robust. Furthermore, qualitative data in the form of open-ended survey responses were analysed using coding methods.\u003c/p\u003e\u003cp\u003eThe analysis included a demographic overview of respondents, their project roles, as well as a comparison of perceived barriers and other aspects. Furthermore, an assessment of the projects\u0026rsquo; economic and societal impacts, and recommendations for improving publicly funded R\u0026amp;D initiatives were assessed. The analysis included comparisons of collaborative projects versus individual projects, as well as responses of the different organisation types. Data processing was conducted manually using Google Sheets and Microsoft Excel, whereby some categories, such as coding results, were cross-referenced and validated with the support of AI-based tools (Gemini, ChatGPT, or Perplexity) to ensure robustness and consistency of interpretation.\u003c/p\u003e\u003cp\u003eAn independent-samples two-tailed Welch \u003cem\u003et\u003c/em\u003e-test assuming unequal variances was conducted in Microsoft Excel to analyse two outcome categories, barriers and project-related aspects, in order to determine whether significant differences exist between collaborative and individual R\u0026amp;D projects (Rochon et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) (Hutcheson \u0026amp; Brown, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Corresponding \u003cem\u003ep\u003c/em\u003e-values and Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e effect sizes were computed (Lenhard \u0026amp; Lenhard, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGiven the targeted selection of respondents of experts and project leaders from research organisations and universities, research institutions, SMEs, and large enterprises, the achieved sample can be considered sufficiently representative for the purposes of this study. Nonetheless, potential sources of bias must be acknowledged. Response bias may arise from the higher likelihood of participation among more engaged or successful actors, while non-response bias may limit the visibility of perspectives from less active or less resourced organisations. These limitations are typical of expert surveys but are mitigated in this study by the diversity of organisation types and roles represented in the final dataset.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Interpreting Results\u003c/h2\u003e\u003cp\u003eThe interpretation of survey results was guided by both descriptive and comparative analysis. Descriptive statistics (means, percentages, distributions) were used to summarize the overall responses and highlight central tendencies across different organisation types. Comparative analysis was employed to examine differences between collaborative projects and individual projects, as well as across organisational categories. Hereby, the percentage of answers by organisation type compared to the total responses, or the mean of rating questions, was frequently used.\u003c/p\u003e\u003cp\u003eAs for the Welch \u003cem\u003et\u003c/em\u003e-test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates a statistically significant difference between group means, while \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.05 indicates no statistically significant difference. As the test is two-tailed, significance applies to differences in either direction; the \u003cem\u003ep\u003c/em\u003e-value is computed with Welch\u0026rsquo;s degrees of freedom and is sensitive to sample size, meaning small effects can reach statistical significance in large samples. Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e quantifies the effect size, with magnitude interpreted as |\u003cem\u003ed\u003c/em\u003e| \u0026lt; 0.20\u0026thinsp;=\u0026thinsp;negligible, 0.20 \u0026le; |\u003cem\u003ed\u003c/em\u003e| \u0026lt; 0.50\u0026thinsp;=\u0026thinsp;small, 0.50 \u0026le; |\u003cem\u003ed\u003c/em\u003e| \u0026lt; 0.80\u0026thinsp;=\u0026thinsp;medium, and |\u003cem\u003ed\u003c/em\u003e| \u0026ge; 0.80\u0026thinsp;=\u0026thinsp;large. For unequal variances, \u003cem\u003ed\u003c/em\u003e is calculated using the pooled average standard deviation.\u003c/p\u003e\u003cp\u003eOpen-ended responses were subjected to qualitative coding, whereby statements were grouped into thematic categories (e.g., barriers to commercialization, perceptions of societal impact). The interpretation approach thus combines statistical rigor with thematic analysis, ensuring that the results reflect both measurable trends and the nuanced perspectives of participants. This mixed approach provides a balanced view, capturing both the generalizability of quantitative findings and the contextual richness of qualitative responses.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Respondent Profile\u003c/h2\u003e\u003cp\u003eThe targeted survey with 1,952 participants resulted in 203 valid responses deriving from project leaders coming from research organisations/universities (26,6%), research institutions (14%), SMEs (42,3%), large corporations (14,9%), and others (2,3%) who participated in R\u0026amp;D projects funded by TA ČR. Some respondents hold multiple positions, resulting in a total of 222 affiliations. The distribution of affiliations can be found in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which shows that SMEs make up the strongest group. This result is consistent with the distribution of TA ČR grants recipients.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of participating organisations\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eDistribution of participating organisation in number of participants\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType of Organisation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResearch Organisation \u0026ndash; University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eResearch Institute\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSME\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLarge Enterprise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e222\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e describes the types of TA ČR programmes to which the survey respondents have participated. A total of 369 selections were made, showing clearly the multiple participations in projects. As reflected in TA ČR statistics, TREND is the largest programme, allowing for the most projects to be generated.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of TA ČR programmes participation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTREND\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTH\u0026Eacute;TA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNCK\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026Eacute;TA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePROSTŘED\u0026Iacute; PRO ŽIVOT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDOPRAVA 2020+\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eZ\u0026Eacute;TA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eDELTA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eGAMA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eKAPPA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParticipants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e369\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31.20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e3.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe respondents\u0026rsquo; organisations\u0026rsquo; main R\u0026amp;D focus related to applied research (57.3%) comprises both industrial research (IR) and experimental development (ED). Commercialisation only reached 20%. Basic research represents only 13.4%, which is in line with TA ČR\u0026rsquo;s focus (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOrganisations\u0026rsquo; main R\u0026amp;D focus\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBasic research\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eApplied research (IR/ED)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProduct commercialization\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParticipants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e302\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the primary roles of the 170 organisations within collaborative projects. The most common role is \u003cem\u003eproject coordination\u003c/em\u003e, followed by that of \u003cem\u003eapplication partner\u003c/em\u003e. Compared with the overall distribution of participants by organisation type, industry actors (SMEs and large enterprises) are more prominently represented as \u003cem\u003eapplication partners\u003c/em\u003e (68.3%) and \u003cem\u003eend-users/commercial partners\u003c/em\u003e (90.2%). In contrast, large enterprises are less engaged in \u003cem\u003eresearch coordination\u003c/em\u003e (7.4%) and \u003cem\u003etechnology development\u003c/em\u003e (6.3%). Research-related organisations dominate in the \u003cem\u003eresearch coordination\u003c/em\u003e function (54.5%), which is consistent with their primary functions. However, SMEs also maintain a notable presence in this role (38.2%), showing a wide engagement in all segments as compared to the large enterprises, which focus more on the \u003cem\u003eapplication partner, project coordinator\u003c/em\u003e, and \u003cem\u003eend-user/commercial partner\u003c/em\u003e role.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWith regard to international collaboration, 71 respondents (35%) reported participating in publicly funded international R\u0026amp;D projects, 14 (7%) were uncertain, and 119 (58%) indicated no engagement in such collaborations. 170 respondents state they have participated in collaborative projects, while 69 have also participated in individual projects without a partner.\u003c/p\u003e\u003cp\u003eIn addition, 170 respondents state that they \u003cem\u003etrack\u003c/em\u003e (42.6%) or \u003cem\u003epartially track\u003c/em\u003e (40.8%) impact indicators (economic or societal) systematically during or after the project, suggesting a strong governance of the projects. The overall satisfaction of the project on a range of 1 (not satisfied) to 5 (very satisfied) reached a mean score of 3.5 across ten rated categories (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The highest satisfaction was reached in \u003cem\u003ecollaboration quality\u003c/em\u003e (4.0) and \u003cem\u003eimplementation feasibility\u003c/em\u003e (4.0), and the least in the \u003cem\u003eadministrative process\u003c/em\u003e (2.8).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSatisfaction rating of project parameters\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean score\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollaboration quality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImplementation feasibility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScientific excellence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocietal relevance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScientific output (scientific articles, methodologies)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarket potential\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCommercial output (intellctual property, license agreements)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFlexibility to adapt the project to unforeseen developments\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe administrative process\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe support in commercialization activities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Perceived Effectiveness\u003c/h2\u003e\u003cp\u003eThe respondents who had participated in collaborative projects were asked to rate the effectiveness of collaborative projects based on several categories on a scale of 1 (ineffective) to 5 (effective). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that \u003cem\u003eestablishing new business units\u003c/em\u003e, such as spin-offs or start-ups, was rated highly ineffective (mean: 1.73), with over half (54%) of respondents rating it as category 1 (least effective). This suggests that establishing new business units is not a primary goal or measure of success in publicly funded collaborative R\u0026amp;D projects. \u003cem\u003eEstablishing long-term partnerships\u003c/em\u003e (mean: 4.1) and \u003cem\u003eachieving technological innovation\u003c/em\u003e (mean: 4.0) were rated as most effective, followed by \u003cem\u003eachieving scientific results\u003c/em\u003e (mean: 3.8). \u003cem\u003eReaching commercialization\u003c/em\u003e achieved a mean score of 3.2.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGiven the clear evidence of inefficiency in establishing new business units, this limitation was examined in greater depth. An evaluation by organisation type took place (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), reflecting consistently low ratings across all groups. Research organisations and universities (mean: 1.88) and research institutes (mean: 2.00) rated the criterion slightly higher than businesses. Nevertheless, the ratings remain low, pointing to persistent challenges in commercializing research results. SMEs (mean: 1.81) provided a comparable rating, likely reflecting their limited resources and experience in establishing new entities. Large enterprises, however, assessed this aspect most critically (mean: 1.48), which can be attributed to their more rigid organisational structures, slower decision-making processes, and reduced flexibility in setting up new business units.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRating distribution of \u003cem\u003eestablishing new business units\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEstablishing new Business Units\u003c/p\u003e\u003cp\u003e(Rating scale)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResearch Organisation - University\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eResearch Institute\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSME\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLarge Enterprise\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Barriers in Publicly Funded R\u0026amp;D Projects\u003c/h2\u003e\u003cp\u003eThe assessment of barriers across collaborative projects and individual projects reveals several general trends, as well as distinct differences in how obstacles are perceived. The rating results by category and project type underlie an analysis as shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. On a scale of 1 (not affected) to 5 (affected significantly), respondents rated barriers for collaborative (170 responses) and individual (69 responses) projects. To examine whether the perceived barriers of the eleven categories differ significantly between collaborative and individual R\u0026amp;D projects, we applied an independent-samples and a two-tailed Welch t-test with unequal variance. The results measure how far apart the means are, relative to the variation within groups.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBarriers of collaborative projects and of collaborative projects calculated as the mean based on the ratings and number of respondents per category.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN_CPs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN_IPs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean_CPs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean_IPs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDistance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSignificant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eCohen's d\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eEffect size interpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMisalignment of goals and priorities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTRUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSMALL\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMisalignment of technical abilities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTRUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSMALL\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntellectual property negotiations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTRUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSMALL\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdministrative burden and bureaucracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFALSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNEGLIGIBLE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTimeline mismatches\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFALSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNEGLIGIBLE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegulatory compliance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFALSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNEGLIGIBLE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCommunication\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFALSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNEGLIGIBLE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDifferences in organisational culture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTRUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSMALL\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFunding instability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFALSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNEGLIGIBLE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInability to adapt the project to changing market needs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFALSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNEGLIGIBLE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProject management\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFALSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNEGLIGIBLE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe most pronounced difference was observed in \u003cem\u003eintellectual property negotiations and agreements\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.001, d\u0026thinsp;=\u0026thinsp;0.55, small effect), where the involvement of multiple independent organisations makes negotiations over licenses, patents, and copyrights more complex, but only with a small effect. Similarly, \u003cem\u003edifferences in organisational culture\u003c/em\u003e were significantly more pronounced in collaborative projects (p\u0026thinsp;=\u0026thinsp;0.004, d\u0026thinsp;=\u0026thinsp;0.41, small effect), reflecting a small level of frictions that arise from integrating diverse working styles, decision-making processes, and priorities across academic and commercial sectors. A statistically significant difference was also identified for \u003cem\u003emisalignment of goals and priorities\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.010, d\u0026thinsp;=\u0026thinsp;0.35, small effect), suggesting that maintaining alignment of objectives is somewhat more difficult in multi-partner projects. Finally, a significant difference was found for \u003cem\u003emisalignment of technical capabilities\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.045, d\u0026thinsp;=\u0026thinsp;0.28, small effect), highlighting the challenges of coordinating heterogeneous technical infrastructures and expertise within collaborative environments.\u003c/p\u003e\u003cp\u003e​​In contrast, no significant differences were found between collaborative projects and individual projects in relation to i\u003cem\u003enability to adapt the project to changing market needs\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.203, d\u0026thinsp;=\u0026thinsp;0.18, negligible effect), \u003cem\u003eproject management\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.227, d\u0026thinsp;=\u0026thinsp;0.17, negligible effect), \u003cem\u003etimeline mismatches\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.260, d\u0026thinsp;=\u0026thinsp;0,16, negligible effect), a\u003cem\u003edministrative burden and bureaucracy\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.458, d\u0026thinsp;=\u0026thinsp;0.11, negligible effect), \u003cem\u003efunding instability\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.525, d\u0026thinsp;=\u0026thinsp;0.09, negligible effect), \u003cem\u003ecommunication\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.637, d\u0026thinsp;=\u0026thinsp;0.07, negligible effect), or \u003cem\u003eregulatory compliance\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.878, d = -0.02, negligible effect). Taken together, these results suggest that collaborative projects are challenged by a small degree more in the categories of intellectual property, goal and priority alignment, technical ability alignment, and organisational culture, whereas other barriers are experienced similarly across project types.\u003c/p\u003e\u003cp\u003eAt the same time, some systemic issues stand out across both contexts. \u003cem\u003eAdministrative burden and bureaucracy\u003c/em\u003e were identified as the most significant barriers in both collaborative projects (mean: 3.06) and individual projects (mean: 2.93), followed by \u003cem\u003etimeline mismatches\u003c/em\u003e (collaborative projects: mean: 2.25; individual projects: mean: 2.07). These findings underline the common challenge of procedural rigidity and misalignment between project needs and funding requirements.\u003c/p\u003e\u003cp\u003eIn the context of individual projects, several additional categories of barriers were asked to be rated, which reflect specific challenges faced by this project type. \u003cem\u003eLimited expertise or skills\u003c/em\u003e (mean: 1.67) and \u003cem\u003ereduced innovation potential\u003c/em\u003e (mean: 1.70) indicate a low level of concern among respondents. Barriers such as \u003cem\u003elimited external validation\u003c/em\u003e or \u003cem\u003enetworking\u003c/em\u003e (mean: 1.97) and \u003cem\u003erestricted access to resources or infrastructure\u003c/em\u003e (mean: 2.00) were rated slightly higher. These factors highlight a level of structural and support limitations impacting individual projects. The most significant barrier within this category was \u003cem\u003elower funding opportunities\u003c/em\u003e (mean: 2.26), underscoring a medium concern of financial constraints as a critical issue for individual project success.\u003c/p\u003e\u003cp\u003eA follow-up question was raised as an open-ended prompt about the specific barriers to the commercialization of the respondents\u0026rsquo; project outcomes. An analysis of the answers resulted in the following highlights. The barriers to commercialization reported in the dataset cluster around five main dimensions: financial/resource constraints, regulatory and administrative hurdles, market/customer dynamics, organisational gaps, and technical/development limitations. A dominant concern is the lack of funding and resources for the final stages of development, such as prototyping, certification, and scaling, which prevents outputs from reaching market readiness. This is compounded by bureaucracy, slow certification processes, and legislative uncertainty, especially in regulated sectors such as healthcare, energy, and construction. Respondents repeatedly stressed that administrative burdens, both at the national and EU level, create inefficiencies and discourage firms, especially SMEs, from pursuing commercialization.\u003c/p\u003e\u003cp\u003eAt the same time, market-related obstacles were strongly emphasized. Conservative customers, competition from low-cost providers, and instability caused by global crises reduce willingness to adopt innovative products. Many organisations admitted internal challenges such as weak commercialization orientation, lack of marketing capacity, or misalignment between academia and industry. Finally, technical barriers emerge because research projects often stop at the prototype stage, leaving a gap between R\u0026amp;D results and commercial products. Together, these findings highlight that commercialization is constrained not by a single factor but by an interplay of systemic financial, institutional, market, and organisational barriers that require more targeted support mechanisms from funding agencies and policymakers.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Aspects Impacting Projects\u003c/h2\u003e\u003cp\u003eA further comparative evaluation of collaborative projects and individual projects was carried out, assessing the views of collaborative (170 responses) and individual (69 responses) project types on specific aspects of R\u0026amp;D projects. Again, on a scale of 1\u0026thinsp;=\u0026thinsp;negative (low/slow/complex/unclear) to 5\u0026thinsp;=\u0026thinsp;positive (high/fast/easy/clear), eleven aspects were rated, and an analysis following independent-samples with a two-tailed Welch t-test with unequal variance was applied (See Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Looking at a negative effect, the three lowest collaborative means were observed for \u003cem\u003emanagement and administration\u003c/em\u003e (mean: 3.34), \u003cem\u003eexpanded funding opportunities\u003c/em\u003e (mean: 3.45), and \u003cem\u003eflexibility in project scope\u003c/em\u003e (mean: 3.52). The lowest individual project means were recorded for \u003cem\u003eexpanded funding opportunities\u003c/em\u003e (mean:2.80), \u003cem\u003eNew long-term partnerships\u003c/em\u003e (mean:3.04), and \u003cem\u003eShared resources and infrastructure\u003c/em\u003e (mean:3.42), indicating different priorities of the project types. Similarly, looking at the positive effects of the aspects, the highest mean ratings were found for \u003cem\u003eNew long-term partnerships\u003c/em\u003e (M\u0026thinsp;=\u0026thinsp;4.03), \u003cem\u003eaccess to complementary expertise and skills\u003c/em\u003e (M\u0026thinsp;=\u0026thinsp;3.95), and \u003cem\u003eIncreased innovation potential\u003c/em\u003e (mean: 3.89), while the lowest IP means were recorded for \u003cem\u003eexpanded funding opportunities\u003c/em\u003e (mean: 2.80), \u003cem\u003enew long-term partnerships\u003c/em\u003e (mean: 3.04), and \u003cem\u003eShared resources and infrastructure\u003c/em\u003e (mean: 3.42).\u003c/p\u003e\u003cp\u003eThe Welch independent-samples \u003cem\u003et\u003c/em\u003e-test revealed statistically significant differences in 10 of the 11 categories examined. IPs were rated significantly higher than CPs in \u003cem\u003eControl over project direction\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), \u003cem\u003eDecision-making processes\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), \u003cem\u003eManagement and administration\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), \u003cem\u003eOwnership of results and IP\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), and \u003cem\u003eFlexibility in project scope\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023). Conversely, CPs were rated significantly higher than IPs in \u003cem\u003eShared resources and infrastructure\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041), \u003cem\u003eIncreased innovation potential\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040), \u003cem\u003eExpanded funding opportunities\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), \u003cem\u003eEnhanced reputation and networking\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and \u003cem\u003eNew long-term partnerships\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Only \u003cem\u003eAccess to complementary expertise and skills\u003c/em\u003e did not show a statistically significant difference between the two groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.315).\u003c/p\u003e\u003cp\u003eEffect sizes (Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e) provide further insight into the magnitude of these differences. Medium effects were found for \u003cem\u003eControl over project direction\u003c/em\u003e (d = \u0026minus;\u0026thinsp;0.583) and \u003cem\u003eDecision-making processes\u003c/em\u003e (d = \u0026minus;\u0026thinsp;0.592), both favouring IPs, as well as for \u003cem\u003eExpanded funding opportunities\u003c/em\u003e (d\u0026thinsp;=\u0026thinsp;0.551) and \u003cem\u003eEnhanced reputation and networking\u003c/em\u003e (d\u0026thinsp;=\u0026thinsp;0.589), both favouring CPs. A large effect was identified for \u003cem\u003eNew long-term partnerships\u003c/em\u003e (d\u0026thinsp;=\u0026thinsp;0.871), again favouring CPs. Small but significant effects were observed for \u003cem\u003eManagement and administration\u003c/em\u003e (d = \u0026minus;\u0026thinsp;0.487), \u003cem\u003eOwnership of results and IP\u003c/em\u003e (d = \u0026minus;\u0026thinsp;0.455), \u003cem\u003eFlexibility in project scope\u003c/em\u003e (d = \u0026minus;\u0026thinsp;0.356), \u003cem\u003eShared resources and infrastructure\u003c/em\u003e (d\u0026thinsp;=\u0026thinsp;0.345), and \u003cem\u003eIncreased innovation potential\u003c/em\u003e (d\u0026thinsp;=\u0026thinsp;0.316). Only \u003cem\u003eAccess to complementary expertise and skills\u003c/em\u003e produced a negligible effect (d\u0026thinsp;=\u0026thinsp;0.163), consistent with its lack of statistical significance.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMean score of aspects of collaborative projects and of individual projects\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN_CPs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN_IPs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean_CPs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean_IPs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDistance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSignificant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eCohen's d\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eEffect size interpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl over project direction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTRUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eMEDIUM\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDecision-making process]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTRUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eMEDIUM\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eManagement and administration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTRUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSMALL\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOwnership of results and IP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTRUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSMALL\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFlexibility in change project scope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTRUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSMALL\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccess to complementary expertise and skills\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFALSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNEGLIGIBLE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShared resources and infrastructure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTRUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSMALL\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncreased innovation potential\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTRUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSMALL\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExpanded funding opportunities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTRUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eMEDIUM\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnhanced reputation and networking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTRUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eMEDIUM\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew long-term partnerships\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTRUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eLARGE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe findings demonstrate a clear distinction between collaborative and individual R\u0026amp;D projects, reflecting a balance between governance-related challenges and external benefits. Individual projects were consistently rated higher in areas related to autonomy and control, particularly \u003cem\u003eControl over project direction\u003c/em\u003e, \u003cem\u003eDecision-making processes\u003c/em\u003e, and \u003cem\u003eOwnership of results and IP\u003c/em\u003e. These differences, supported by medium effect sizes, indicate that participants in individual projects experience greater independence and clarity in steering the project and determining its outputs. The higher rating of \u003cem\u003eManagement and administration\u003c/em\u003e among IPs, despite their smaller scale, suggests that administrative processes are perceived as less burdensome in collaborative settings, though the significant difference indicates that organisational structures may still be viewed differently across project types.\u003c/p\u003e\u003cp\u003eConversely, collaborative projects were rated significantly higher in categories that capture external advantages and long-term strategic benefits. These included \u003cem\u003eExpanded funding opportunities\u003c/em\u003e, \u003cem\u003eEnhanced reputation and networking\u003c/em\u003e, and especially \u003cem\u003eNew long-term partnerships\u003c/em\u003e, which showed a large effect size and the most pronounced distinction between project types. These findings confirm that collaborative R\u0026amp;D projects provide a platform for resource pooling, visibility, and enduring institutional connections that are less accessible to individual projects. The higher scores for \u003cem\u003eShared resources and infrastructure\u003c/em\u003e and \u003cem\u003eIncreased innovation potential\u003c/em\u003e further reinforce this interpretation, even though their effect sizes were small.\u003c/p\u003e\u003cp\u003eInterestingly, \u003cem\u003eAccess to complementary expertise and skills\u003c/em\u003e did not differ significantly between project types, with both collaborative and individual projects assigning high ratings to this category. This suggests that access to specialized knowledge is widely valued across all R\u0026amp;D settings and may not be exclusive to collaborative environments. Individual projects may achieve such expertise through alternative mechanisms such as contracting or partnerships short of full-scale collaboration.\u003c/p\u003e\u003cp\u003eOverall, the results point to a governance\u0026ndash;benefit trade-off. While collaborative projects impose additional complexity and reduce autonomy, they offer significant advantages in terms of reputation, funding, and the creation of durable networks. These differences are not only statistically significant but also practically meaningful, as indicated by the medium-to-large effect sizes in several categories. From a policy and management perspective, this suggests that maximizing the value of collaboration requires measures to mitigate administrative burdens and decision-making challenges, thereby allowing projects to capitalize on the substantial external benefits that collaboration offers.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Economic Impact\u003c/h2\u003e\u003cp\u003eThe results (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) demonstrate that publicly funded R\u0026amp;D projects generate a range of economic outcomes, though their frequency and distribution differ strongly across organisation types. The most dominant impact across the dataset is the \u003cem\u003elaunch of new products or services\u003c/em\u003e, reported 154 times (76% of all responses). This underlines the primary role of such projects in fostering product development and innovation. The second most frequent outcome is \u003cem\u003eexpected economic impact within two years\u003c/em\u003e (105 responses, 52%), showing strong potential for near-term returns. Other notable impacts include \u003cem\u003ejobs created\u003c/em\u003e (61 responses, 30%) and \u003cem\u003epatent applications filed\u003c/em\u003e (64 responses, 32%), indicating contributions to both market growth and intellectual property generation. Exploring the economic impact by organisation type, the following highlights are found:\u003c/p\u003e\u003cp\u003eResearch Organisations - Universities show their strongest impact in terms of total number in the areas of \u003cem\u003enew products/services launched\u003c/em\u003e (34; 22%), patent applications (19; 30%). They also report meaningful shares in expected economic impact within two years (23; 22%) and jobs created (14; 23%). This reflects their dual role of advancing innovation while transferring knowledge to industry via patents and licenses. Research Institutes show similar but slightly lower results than Research Organisations - Universities, with 20 new \u003cem\u003eproducts/services launched\u003c/em\u003e (13%), 13 \u003cem\u003epatents filed\u003c/em\u003e (20%), and 18 \u003cem\u003eexpected impacts within two years\u003c/em\u003e (17%). Their contributions are primarily focused on knowledge generation and applied research, serving as a bridge between academia and industry.\u003c/p\u003e\u003cp\u003eSMEs consistently dominate commercialization-related categories: \u003cem\u003enew products/services launched\u003c/em\u003e (49.0%), \u003cem\u003emarket expansion\u003c/em\u003e (60.0%), jobs created (45.9%), and cost savings/process efficiency (49.3%). They also account for half of those \u003cem\u003eexpecting economic impact within two years\u003c/em\u003e (49.5%). This positions SMEs as the most agile and market-oriented actors, leveraging public R\u0026amp;D to deliver tangible, near-term economic effects. A weakness is seen in the \u003cem\u003elicense contracts concluded\u003c/em\u003e (25.0%), likely because they exploit their own research results for competitive advantages instead of transferring them to others. Large Enterprises report more modest contributions, e.g., 25 new products/services launched (16%) and 12 expected impacts (11%). Their role is less prominent in jobs created (7; 11%) and market expansion (7; 14%), suggesting that large enterprises benefit less directly in measurable short-term outcomes from public R\u0026amp;D. They nevertheless maintain relevance in patents (9; 14%), reflecting integration into incremental innovation pipelines.\u003c/p\u003e\u003cp\u003eWeak areas across all organisation types include \u003cem\u003eprivate investment attraction\u003c/em\u003e (9 responses, 4%), with SMEs (44%) and universities (33%) contributing the most. Furthermore, \u003cem\u003estart-up/spin-off creation\u003c/em\u003e is also limited (11 responses, 5%), led mainly by universities (45%) and SMEs (36%), while large enterprises report none. \u003cem\u003eNo economic impact expected\u003c/em\u003e was noted in 29 cases (14%), concentrated among universities (38%) and SMEs (34%), suggesting that not all projects translate into measurable economic benefits in the short term or at all.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eExpected economic impact\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEconomic Impact\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eDistribution of participating organisation in number of participants\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e\u003cp\u003eDistribution of participating organisation in %\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParticipants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e26.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e14.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e42.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e14.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResearch Organisation - University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eResearch Institute\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSME\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLarge Enterprise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTotal count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eResearch Organisation - University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eResearch Institute\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSME\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eLarge Enterprise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eTotal %\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCost savings/process efficiency achieved\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e49.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e14.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e33.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEconomic impact to be expected within 2 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e21.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e17.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e49.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e11.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e51.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJobs created\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e45.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e11.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e30.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLicense contracts concluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e43.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e25.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e25.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e15.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarket expansion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e60.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e14.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e24.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew products/ services launched\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e22.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e48.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e16.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e75.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo economic impact expected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e37.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e17.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e34.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e10.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e14.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNotable revenue increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e22.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e14.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e51.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e12.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e24.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatent applications filed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e29.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e35.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e14.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e31.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrivate investment attracted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e33.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e44.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e11.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e4.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStart-up/spin-off creation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e45.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e36.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e5.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFurthermore, respondents reflected on the expected economic impact based on collaboration types: university-industry collaborations (UIC), industry-only, and research-only projects, and on a scale of 1 (no impact) to 5 (significant Impact), as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The data indicate that UIC projects demonstrate the highest concentration of economic impact within three years of completion, with modal responses at levels 3 and 4. In contrast, industry-only projects exhibit a more even distribution across the scale, with moderate impacts prevailing but fewer instances of significant impact (level 5). Research-only projects predominantly register at the lower to mid-range of the scale (levels 2 and 3), suggesting limited translation into measurable economic outcomes. These findings highlight the comparatively stronger economic performance of collaborative R\u0026amp;D projects, underscoring the value of cross-sectoral partnerships in enhancing the economic relevance of publicly funded research.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFinally, participants were asked to reflect on their experience regarding which type of projects they considered most likely to achieve the highest economic impact. The responses revealed a clear preference for collaborative projects, whether with a single partner, multiple partners, or international partners, cited by a total of 64.4% of respondents. Reinforcing this question, the respondents stated that their preferred type of project was also these three types of projects (69.2%) over the other choices, including individual projects and industry or research-only projects.\u003c/p\u003e\u003cp\u003eTo deepen this finding, both questions were complemented by an open-ended prompt asking respondents to explain their choice. Due to the similarity of the questions, a combined analysis was carried out by developing a structured codebook. Findings reveal that the type of project most likely to achieve economic impact is defined less by its formal structure and more by the quality of its participants, leadership, and focus. One respondent states: \u003cem\u003e\u0026ldquo;Project success depends on the current project situation, not the general type\u0026ldquo;\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eRespondents are clear that company involvement is indispensable: \u003cem\u003e\u0026ldquo;The project leader should always be a company with greater market knowledge than research organisations.\u0026rdquo; \u0026ldquo;If a company leads, there is greater focus on commercialization outcomes.\u0026rdquo;\u003c/em\u003e These statements indicate that firms bring market orientation, commercialization capacity, and the financial motivation needed to ensure implementation. Research organisations complement this by contributing scientific expertise, analytical capabilities, and infrastructure, but they are rarely able to drive commercialization alone.\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;Simplicity of the project scheme is important. Many partners make communication too difficult. More partners only for large projects that are beyond the capabilities of 1 or 2 partners\u0026rdquo;\u003c/em\u003e, states another respondent. The optimal configuration is often a small, focused consortium consisting of one research organisation and one or two companies, which strikes the right balance between knowledge creation and practical application. Larger or international projects can extend reach, bring diverse perspectives, and open new markets. Still, they require careful governance to avoid conflicts over intellectual property, divergent interests, and heavy administrative overhead.\u003c/p\u003e\u003cp\u003eBeyond structure, respondents stress that project management, goal clarity, and orientation toward higher TRLs are decisive factors for economic success. Projects led by companies tend to ensure stronger market alignment, faster decision-making, and more efficient resource allocation. Applied research with clear commercialization pathways is rated far more impactful than exploratory research without market connections. At the same time, respondents emphasize the need for flexibility and context sensitivity: in some fields, long-term projects of three to four years are essential, while in others, leaner setups are more appropriate. Rigid administrative criteria and bureaucratic evaluation metrics are widely criticized as barriers that undermine potential. Taken together, the findings suggest that economic impact emerges most reliably when programmes foster company-led, application-focused collaborations with research organisations, supported by flexible funding frameworks that accommodate different project contexts while reducing bureaucratic burdens.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.6. Societal Impact\u003c/h2\u003e\u003cp\u003eSocietal outcomes attributed to the respondents\u0026rsquo; projects are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. It highlights that the top three societal outcomes are \u003cem\u003eskills development/education\u003c/em\u003e (28.2%) and \u003cem\u003eenvironmental benefits\u003c/em\u003e (24.4%), and \u003cem\u003eimproved public health/safety\u003c/em\u003e (13.9%) are the highest-ranked societal outputs, while the lowest responses include \u003cem\u003ecitizen engagement/co-creation\u003c/em\u003e (3.1%) and \u003cem\u003eno societal impact expected\u003c/em\u003e (3.1%). With the drive for digitalization, it is surprising that \u003cem\u003edigital inclusion\u003c/em\u003e, with 8.7% is resulting in a low impact.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eParticipants were also asked to assess which type of project is most likely to achieve a successful societal impact. The results confirmed once again that UICs, whether with one or multiple partners, were perceived as having the most significant impact (53.2%). In contrast, 21.2% of respondents indicated that all project types, whether collaborative projects, individual projects, or industry- and research-only initiatives, have an equal chance of generating societal impact. To deepen this insight, a follow-up question invited respondents to explain their reasoning. The qualitative analysis of these responses led to the development of structured codes, which are summarized below.\u003c/p\u003e\u003cp\u003eThe analysis of responses reveals that societal impact is closely tied to inclusiveness, diversity, and practical application. Projects involving a broader set of partners, companies, research organisations, public institutions, and in some cases, NGOs or communities, are seen as particularly effective, since they combine different competencies and perspectives while ensuring that outcomes are relevant to wider societal needs. \u003cem\u003e\u0026ldquo;Collaborative projects where multiple partners work together have the greatest chance\u0026rdquo;\u003c/em\u003e, says one respondent, and another: \u003cem\u003e\u0026ldquo;Projects where more different spheres are connected bring higher social impact.\u0026rdquo;\u003c/em\u003e International or cross-sectoral collaborations are also valued, as they bring in fresh viewpoints and open access to new societal contexts.\u003c/p\u003e\u003cp\u003eRespondents emphasize that applied research with a clear orientation toward public good has the most significant potential to generate tangible societal benefits. Unlike projects driven solely by commercial objectives, initiatives designed to address real-world challenges, strengthen public services, or raise societal awareness are considered more impactful. Effective collaboration dynamics, including mutual respect, trust, and the integration of corporate and social perspectives, are considered essential enablers of this process. As one participant puts it: \u003cem\u003e\u0026ldquo;Mutual understanding and respect for corporate and social needs lead to impact.\u0026rdquo;\u003c/em\u003e At the same time, many answers are rooted in personal experience, and several respondents acknowledge the difficulty of assessing societal impact, underlining its multidimensional and context-dependent nature, and or admit uncertainty. Overall, societal impact is perceived not only as an outcome of research and innovation but also as a product of the way partnerships are structured and governed. \u003cem\u003e\u0026ldquo;Connecting a business and a public organisation ensures social relevance.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study contributes to the growing body of research on the impact of publicly funded R\u0026amp;D by providing empirical insights from project leaders in the Czech Republic. The findings align with literature that highlights the importance of collaboration as a determinant of impact, particularly within the framework of the Triple and Quadruple Helix models (Etzkowitz \u0026amp; Leydesdorff, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) (Cunningham \u0026amp; G\u0026ouml;k, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Addressing the first research question on preferred project structures and perceived potential for economic impact, respondents consistently emphasized that collaborative projects, especially those involving UICs with either one, multiple, or international partners, are perceived as most effective in achieving economic impact. These were considered more likely than individual projects to generate tangible outcomes such as new products, services, and market expansion, while also fostering long-term networking and partnerships. By contrast, individual projects were valued for their autonomy, flexibility, and clarity over intellectual property, but were seen as less capable of generating systemic integration or long-term economic value. This confirms that the composition and structure of consortia play a central role in shaping commercialization potential.\u003c/p\u003e\u003cp\u003eAddressing the second research question on economic and societal impacts participants expect from publicly funded R\u0026amp;D projects implies that the role of different organisation types further underlines differentiated contributions within the innovation system. SMEs emerged as the most agile commercialisers of research results, leading in product launches, job creation, and market expansion, whereas universities and research institutes contributed more significantly to patents, licenses, and knowledge-based outputs that underpin future innovation. Large enterprises, by contrast, appear to rely on incremental integration of R\u0026amp;D outcomes, reporting fewer short-term impacts. This division of roles suggests that funding programs should acknowledge the distinct capacities of different actors rather than adopt one-size-fits-all approaches. In terms of societal impacts, the findings highlight that projects involving multiple stakeholders, business, academia, public sector, and civil society, tend to generate greater societal value, especially in education, skills development, environmental sustainability, and public health. However, areas such as digital inclusion and citizen engagement were reported less frequently, suggesting uneven distribution of societal benefits across domains. Notably, respondents stressed that societal impacts often materialize over longer time horizons and depend on effective collaboration, which cannot be captured solely through short-term economic metrics.\u003c/p\u003e\u003cp\u003eThe third research question, focusing on barriers influencing the realization of economic and societal impacts from publicly funded R\u0026amp;D projects, is addressed through the systemic challenges that continue to constrain the realization of both economic and societal outcomes. Respondents repeatedly pointed to bureaucracy, administrative burden, and inflexible project rules as major obstacles, echoing findings from OECD (OECD, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) on inefficiencies in R\u0026amp;D governance. Certification delays and regulatory uncertainty, especially in heavily regulated sectors such as healthcare, construction, and energy, were identified as further bottlenecks. The weak performance in spin-off creation and private investment attraction across all organisations highlights structural gaps in translating research into entrepreneurship and mobilizing private capital. At the same time, external shocks, such as the COVID-19 pandemic, geopolitical crises, and energy price volatility, were seen as additional barriers to market uptake, even when technological readiness had been achieved. Enabling factors identified by respondents included well-structured partnerships, strong company involvement, realistic timelines, and support for higher-TRL projects, all of which enhance commercialization prospects and consequently impact.\u003c/p\u003e\u003cp\u003eFurthermore, to address societal impact, administrative and financial requirements should be adapted to lower barriers for diverse actors such as NGOs, municipalities, and community organisations, for instance, through lighter reporting or tailored cost models. At the same time, funding calls should explicitly integrate societal impact criteria, assessing how projects contribute to well-being, sustainability, inclusiveness, and social innovation alongside scientific and economic outcomes.\u003c/p\u003e\u003cp\u003eOverall, the analysis suggests that funding agencies need to shift from rigid, one-size-fits-all programme designs toward more flexible, performance and impact-oriented schemes. Respondents consistently highlight that economic impact depends less on the formal type of project and more on how well partnerships are structured, managed, and aligned with market needs. This means that funding instruments should prioritize company involvement and leadership, since companies provide the most substantial incentives and capacities for commercialization. At the same time, programmes should create room for small, focused consortia of one research organisation and one or two companies, which are widely viewed as the most effective structure for balancing scientific depth with market orientation. While large consortia and international collaborations can be valuable, they also carry higher risks of coordination problems and knowledge-protection conflicts, necessitating tailored support mechanisms such as mediation, IP-sharing templates, or regulatory guidance.\u003c/p\u003e\u003cp\u003eFunding agencies should also emphasize quality, competence, and realistic timelines over rigid formal requirements. Instead of administrative-heavy evaluation metrics (such as quotas or formal indicators not directly tied to outcomes), assessment should focus on clarity of goals, commercialization pathways, and the demonstrated capabilities of partners. Programmes should explicitly support projects at higher technology readiness levels (TRLs), where the likelihood of economic impact is greatest, while still maintaining a pipeline for earlier-stage research. Longer project durations (three to four years in specific fields) should be allowed where needed, avoiding artificial pressure for shorter cycles that cannot deliver meaningful outcomes. Finally, funding frameworks should embed market access, certification, and internationalization pathways, enabling research results to cross into practice more effectively. In short, funding design should become more context-sensitive and impact-driven, ensuring that programme structures actively facilitate, not hinder, the translation of research into tangible economic and societal value.\u003c/p\u003e\u003cp\u003eThis study highlights several directions for further investigation. Future research should adopt longitudinal approaches to capture medium- and long-term outcomes beyond project completion, as many societal benefits and spillovers emerge only after several years. Comparative analyses across EU countries and regions would also clarify how different innovation systems and governance structures shape funding effectiveness. Additional work is also needed on sector-specific dynamics, particularly in regulated fields such as healthcare, energy, and construction, where certification and market access are significant barriers. In addition, future studies should examine why publicly funded projects yield relatively few spin-offs and start-ups, and how entrepreneurship can be better supported. Finally, improved frameworks for measuring societal impact, stronger integration of non-traditional actors (e.g., NGOs, municipalities), and analysis of global disruptions such as pandemics or war could deepen understanding of how public R\u0026amp;D creates sustainable economic and societal value.\u003c/p\u003e\u003cp\u003eThis study has several limitations. As a cross-sectional survey, it captures project leaders\u0026rsquo; perceptions at a single point in time, which may not fully reflect long-term economic or societal impacts. Reliance on self-reported data introduces subjectivity and possible recall bias, while non-response bias may have led to an overrepresentation of more successful or engaged actors. Moreover, focusing solely on TA ČR-funded projects limits the generalizability of the findings to other funding schemes or national contexts. A further limitation arises from the fact that the survey was addressed to entire organisations rather than to individual project leaders. In the case of larger institutions, such as universities, respondents were often unable to represent data from the organisation as a whole, meaning that some responses may be based on individual estimations or were left unanswered, potentially reducing comparability across organisation types.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study has shown that publicly funded R\u0026amp;D projects in the Czech Republic generate both economic and societal impacts, but their effectiveness is strongly conditioned by project structure, organisational roles, and systemic barriers. Collaborative projects, particularly those involving UICs, emerge as the most impactful in terms of economic outcomes and societal relevance, while individual projects provide autonomy and efficiency but limited broader integration. SMEs act as central drivers of commercialization, whereas research organisations, universities, and research institutes, contribute knowledge-based foundations that enable future innovation.\u003c/p\u003e\u003cp\u003eThe barriers identified, bureaucracy, lack of ambition in new business unit creation, certification delays, funding gaps after project completion, and market conservatism, are systemic issues that undermine the efficiency of public R\u0026amp;D investment. Addressing these requires programme designs that go beyond funding allocation to actively support commercialization pathways, reduce administrative burdens, and embed mechanisms for business unit creation and societal value creation. Funding agencies should prioritize company involvement and applied research, support small but effective consortia, and adapt project durations to sectoral needs.\u003c/p\u003e\u003cp\u003eFrom a policy perspective, the findings suggest that the impact of public R\u0026amp;D can be maximized by designing flexible, context-sensitive, and impact-oriented funding schemes. These should balance economic and societal objectives, incentivize high-quality collaboration, and provide tailored support for commercialization and social uptake. Future research should build on this survey by conducting comparative studies across EU member states and by combining quantitative surveys with in-depth case studies to capture the full spectrum of public R\u0026amp;D outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthical Approval and Consent to Participate\u003c/h2\u003e\n\u003cp\u003eAll participants provided written informed consent in accordance with the requirements of the Ethics Committee of the corresponding author\u0026rsquo;s affiliated institution.\u003c/p\u003e\n\u003ch2\u003eConsent for Publication\u003c/h2\u003e\n\u003cp\u003eNo identifiable personal information of the survey participants is included in the manuscript. All authors provide consent for the publication of the manuscript.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial or non-financial interests that could have influenced the work reported in this paper. The work received no funding.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eU.M-S. and M.K. have equally contributed to the design of the survey and the collection of its results. M.K. has prepared the majority of the computational analysis, whereas U.M-S. has prepared the majority of the literature review and manuscript. Both have complemented each other\u0026apos;s work. M.B. and P.K. have jointly supervised and reviewed the work.\u003c/p\u003e\n\u003ch2\u003eAcknowledgment\u003c/h2\u003e\n\u003cp\u003eThe authors gratefully acknowledge the valuable contributions of all survey participants and the support of the colleagues of the Technology Agency of the Czech Republic for this study. The manuscript benefited from AI-assisted proofreading provided by Grammarly, OpenAI\u0026rsquo;s ChatGPT, which helped refine spelling, grammar, and sentence structure. The final version reflects the authors\u0026rsquo; independent revisions.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData is provided within the manuscript with supplementary files being available from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlmus, M., \u0026amp; Czarnitzki, D. 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(2015). The Entrepreneurial State: Debunking Public vs. Private Sector Myths. \u003cem\u003eJournal of Entrepreneurship and Public Policy\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(3), 392\u0026ndash;394. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/JEPP-04-2014-0017\u003c/span\u003e\u003cspan address=\"10.1108/JEPP-04-2014-0017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Research impact assessment, economic impact of R\u0026D, societal impact of R\u0026D, university–industry collaboration (UIC), knowledge and technology transfer (KTT), impact indicators (KPIs)","lastPublishedDoi":"10.21203/rs.3.rs-7878204/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7878204/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePublic research and development (R\u0026amp;D) funding plays a crucial role in driving innovation and economic growth in Europe, supported by multilevel frameworks including EU-wide and national funding schemes. This study investigates the experiences and perceptions of participants engaged in publicly funded R\u0026amp;D projects administered by the Technology Agency of the Czech Republic (TA ČR), focusing on economic and societal impacts. A comprehensive survey of 203 project leaders from diverse organisations examined three key questions: preferences for project structures, expected economic and societal impacts, and barriers and enablers in realizing project outcomes. Findings indicate a distinct advantage of collaborative projects, especially university-industry collaborations, in generating significant economic benefits such as new products, market expansion, and job creation alongside societal benefits, including skills development and environmental improvements. SMEs emerged as primary drivers of commercialization, while research organisations contributed to knowledge-based outputs. Barriers such as bureaucracy, intellectual property negotiations, and certification delays constrain impact realization. The study recommends flexible, impact-driven funding mechanisms prioritizing company-led collaborations and tailored support for commercialization and societal value creation. These insights contribute to enhancing the design of public R\u0026amp;D funding policies to maximize innovation impact and sustainability.\u003c/p\u003e","manuscriptTitle":"Economic and Societal Impacts of Publicly Funded R\u0026amp;D: Evidence from Project Leaders in the Czech Republic","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 07:55:58","doi":"10.21203/rs.3.rs-7878204/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"73e59512-188f-4e00-a752-8fdf4a504fa6","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:07:02+00:00","versionOfRecord":{"articleIdentity":"rs-7878204","link":"https://doi.org/10.1186/s43093-026-00806-9","journal":{"identity":"future-business-journal","isVorOnly":false,"title":"Future Business Journal"},"publishedOn":"2026-04-16 15:58:00","publishedOnDateReadable":"April 16th, 2026"},"versionCreatedAt":"2025-11-13 07:55:58","video":"","vorDoi":"10.1186/s43093-026-00806-9","vorDoiUrl":"https://doi.org/10.1186/s43093-026-00806-9","workflowStages":[]},"version":"v1","identity":"rs-7878204","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7878204","identity":"rs-7878204","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}