Beyond STI: A Multi-Layered Evolutionary Framework for Innovation Dynamics in Lagging Regions

Beyond STI: A Multi-Layered Evolutionary Framework for Innovation Dynamics in Lagging Regions | 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 Beyond STI: A Multi-Layered Evolutionary Framework for Innovation Dynamics in Lagging Regions Burç Arslan Kaleli This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7012025/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Persistent regional disparities in innovation performance remain a longstanding challenge for the European Union, particularly in structurally weaker territories classified as Emerging and Moderate Innovators. Conventional Science–Technology–Innovation (STI) models, with their emphasis on formal R&D, patents, and codified knowledge, often yield limited traction in these contexts. This paper develops a Multi-Layered Evolutionary Innovation Framework that captures how micro-level firm behaviors, meso-level regional innovation system structures, and macro-institutional capacities jointly shape innovation outcomes. Leveraging a panel of 245 NUTS-2 regions from 2016 to 2023, the analysis employs fixed-effects regression models with interaction terms to assess the role of Doing–Using–Interacting (DUI) mechanisms—centered on tacit knowledge, experiential learning, and informal collaboration. Results indicate that DUI serves as a consistently robust and temporally resilient driver of innovation, particularly in regions where formal STI infrastructures are underdeveloped. Furthermore, hybrid strategies that combine DUI and STI yield the highest returns, though this complementarity materializes only under conditions of strong institutional embeddedness. The effectiveness of innovation modes displays considerable heterogeneity across regional contexts, shaped by non-linear, path-dependent interactions with macro-level absorptive capacities, proxied by human capital endowments. These findings challenge the prevailing STI-centric policy paradigm and point to the necessity of adopting evolutionary, context-sensitive, and multi-scalar innovation strategies. In doing so, the study provides actionable empirical insights and conceptual contributions for designing policies that elevate informal, practice-based innovation alongside formal scientific processes, particularly in structurally disadvantaged regions. JEL Codes: O18; O31; O33; R11; C23 Regional Innovation Systems DUI and STI Innovation Modes Innovation in Lagging Regions Hybrid Innovation Strategies Innovation Policy Institutional Embeddedness Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7012025","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":483298048,"identity":"31a11c8d-8a74-4220-a8c2-82b928a4c8b6","order_by":0,"name":"Burç Arslan Kaleli","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIie3PvQrCMBDA8ZSD63La9QTRV1AKfqAPU5e6tG8g6pSpdK5voYuzUOwzdFQKDk6K4CimTk41boL5Q8IN94NECJPpB+uoYy3FQNQBdmrmpi5hgYBeSegLIqicxWfS5/B4TWbcQptu53w2IGGn+3UVGSZTt7HO2EWobUdBph5Gvp9XPiz30TogT6QiboCKMPU0yIMXEujkBg9dspHsIRAUodQgw+gEjVXMXQnYgzBmwk9/6du+dY3u47bjpMUtuM9bjp1mleQ95Netu14Gl2+2TSaT6X96AlHKPMGObENkAAAAAElFTkSuQmCC","orcid":"","institution":"Yeditepe University","correspondingAuthor":true,"prefix":"","firstName":"Burç","middleName":"Arslan","lastName":"Kaleli","suffix":""}],"badges":[],"createdAt":"2025-06-30 15:08:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7012025/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7012025/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89554912,"identity":"d5525004-16bb-4dc0-88cf-78ddf2d78513","added_by":"auto","created_at":"2025-08-21 09:09:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":729391,"visible":true,"origin":"","legend":"","description":"","filename":"BeyondSTIAMultiLayeredEvolutionaryFrameworkforInnovationDynamicsinLaggingRegionsKaleliSubmissionv2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7012025/v1_covered_a36b28df-3279-4612-af94-08ab564cf8b6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Beyond STI: A Multi-Layered Evolutionary Framework for Innovation Dynamics in Lagging Regions","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"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":"Regional Innovation Systems, DUI and STI Innovation Modes, Innovation in Lagging Regions, Hybrid Innovation Strategies, Innovation Policy, Institutional Embeddedness","lastPublishedDoi":"10.21203/rs.3.rs-7012025/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7012025/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePersistent regional disparities in innovation performance remain a longstanding challenge for the European Union, particularly in structurally weaker territories classified as Emerging and Moderate Innovators. Conventional Science–Technology–Innovation (STI) models, with their emphasis on formal R\u0026amp;D, patents, and codified knowledge, often yield limited traction in these contexts. This paper develops a Multi-Layered Evolutionary Innovation Framework that captures how micro-level firm behaviors, meso-level regional innovation system structures, and macro-institutional capacities jointly shape innovation outcomes. Leveraging a panel of 245 NUTS-2 regions from 2016 to 2023, the analysis employs fixed-effects regression models with interaction terms to assess the role of Doing–Using–Interacting (DUI) mechanisms—centered on tacit knowledge, experiential learning, and informal collaboration. Results indicate that DUI serves as a consistently robust and temporally resilient driver of innovation, particularly in regions where formal STI infrastructures are underdeveloped. 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