{"?xml":{"@version":"1.0"},"edm:RDF":{"@xmlns:dc":"http://purl.org/dc/elements/1.1/","@xmlns:edm":"http://www.europeana.eu/schemas/edm/","@xmlns:wgs84_pos":"http://www.w3.org/2003/01/geo/wgs84_pos","@xmlns:foaf":"http://xmlns.com/foaf/0.1/","@xmlns:rdaGr2":"http://rdvocab.info/ElementsGr2","@xmlns:oai":"http://www.openarchives.org/OAI/2.0/","@xmlns:owl":"http://www.w3.org/2002/07/owl#","@xmlns:rdf":"http://www.w3.org/1999/02/22-rdf-syntax-ns#","@xmlns:ore":"http://www.openarchives.org/ore/terms/","@xmlns:skos":"http://www.w3.org/2004/02/skos/core#","@xmlns:dcterms":"http://purl.org/dc/terms/","edm:WebResource":[{"@rdf:about":"http://www.dlib.si/stream/URN:NBN:SI:doc-DSA91304/4955805d-835c-4e10-aba4-8084eccd0b73/PDF","dcterms:extent":"512 KB"},{"@rdf:about":"http://www.dlib.si/stream/URN:NBN:SI:doc-DSA91304/263e1067-7552-467e-bbfd-88cbe65f07f1/TEXT","dcterms:extent":"0 KB"}],"edm:ProvidedCHO":{"@rdf:about":"URN:NBN:SI:doc-DSA91304","dcterms:issued":"2026","dc:creator":["Popovska, Jasmina","Umek, Lan"],"dc:format":[{"@xml:lang":"sl","#text":"letnik:24"},{"@xml:lang":"sl","#text":"številka:iss. 1"},{"@xml:lang":"sl","#text":"str. 245-281"}],"dc:identifier":["DOI:10.17573/cepar.2026.1.09","ISSN:2591-2240","COBISSID_HOST:281656579","URN:URN:NBN:SI:doc-DSA91304"],"dc:language":"en","dc:publisher":{"@xml:lang":"sl","#text":"= University of Ljubljana Press"},"dc:source":{"@xml:lang":"sl","#text":"Central European Public Administration Review"},"dc:subject":[{"@xml:lang":"sl","#text":"analiza ovojnice podatkov"},{"@xml:lang":"en","#text":"data envelopment analysis"},{"@xml:lang":"en","#text":"feature selection"},{"@xml:lang":"en","#text":"innovation efficiency"},{"@xml:lang":"en","#text":"innovation indicators"},{"@xml:lang":"en","#text":"innovation policy"},{"@xml:lang":"sl","#text":"inovacijska politika"},{"@xml:lang":"sl","#text":"inovacijska učinkovitost"},{"@xml:lang":"sl","#text":"inovacijski kazalniki"},{"@xml:lang":"sl","#text":"izbira značilk"}],"dc:title":{"@xml:lang":"sl","#text":"Advancing DEA-based assessment of innovation efficiency through feature selection|"},"dc:description":[{"@xml:lang":"sl","#text":"Purpose: This article investigates innovation efficiency in European Union (EU) countries and addresses methodological inconsistencies in previous research. It evaluates how efficiently national innovation systems (NISs) convert innovation-related inputs into measurable outputs, with the aim of improving the reliability and interpretability of efficiency assessments. Design/Methodology/Approach: To identify a parsimonious and statistically relevant set of indicators, the study employs Multi-Cluster Feature Selection (MCFS), a hybrid method that combines unsupervised clustering with the supervised Least Absolute Shrinkage and Selection Operator (LASSO). The technique is applied to longitudinal data derived from the European Innovation Scoreboard (EIS), resulting in a consistent subset of thirteen indicators encompassing key stages of the innovation process. Following indicator selection, a two-stage Data Envelopment Analysis (DEA) model is applied to assess efficiency at both the technological/ knowledge-production stage and the commercialisation stage. This approach supports differentiation between countries that are efficient in generating knowledge outputs and those that are effective in converting these outputs into economic results. Findings: The findings indicate substantial variation in innovation efficiency across EU countries. Few countries achieve high efficiency at both stages, highlighting the difficulty of sustaining performance across the entire innovation value chain. The analysis reveals persistent inefficiencies, particularly at the commercialisation stage, consistent with previous research emphasising structural barriers to translating research and development outputs into economic gains. The results also demonstrate that differences in country rankings reported in the literature are often attributable to differences in indicator selection and DEA model specification. Even among studies using similar DEA frameworks, variation in indicator inclusion leads to different classifications of country performance. Practical Implications: The methodological choices improve comparability across countries and over time, while also reducing complexity. The two-stage DEA structure provides policymakers with further insight into the internal functioning of national innovation systems and the sources of inefficiency, particularly at the commercialisation stage. This enables more targeted policy interventions that distinguish between weaknesses in knowledge production and weaknesses in the economic exploitation of innovation outputs. Originality/Value: This study contributes to the literature by introducing MCFS into the field of innovation efficiency assessment and by offering a streamlined, empirically justified set of indicators suitable for DEA applications. By combining a data-driven feature selection approach with a two-stage DEA model, the article addresses methodological fragmentation in previous research and provides a more transparent and replicable framework for evaluating national innovation systems"},{"@xml:lang":"sl","#text":"Namen: članek preučuje inovacijsko učinkovitost držav članic Evropske unije (EU) in obravnava metodološke nedoslednosti v dosedanjih raziskavah. Ovrednoti, kako učinkovito nacionalni inovacijski sistemi (NIS) pretvarjajo vložke, povezane z inovacijami, v merljive rezultate, s ciljem izboljšati zanesljivost in interpretabilnost ocen učinkovitosti. Zasnova/metodologija/pristop: za določitev statistično relevantnega nabora kazalnikov študija uporablja metodo MCFS (Multi-Cluster Feature Selection – MCFS), ki je hibridna metoda, ki združuje nenadzorovano razvrščanje v skupine in nadzorovano regresijo (Least Absolute Shrinkage and Selection Operator – LASSO). Tehnika je uporabljena na longitudinalnih podatkih iz Evropskega inovacijskega pregleda (European Innovation Scoreboard – EIS), kar je privedlo do konsistentnega nabora trinajstih kazalnikov, ki zajemajo ključne stopnje inovacijskega procesa. Po izbiri kazalnikov je uporabljena dvostopenjska metoda podatkovne ovojnice (Data Envelopment Analysis – DEA) za oceno učinkovitosti tako na stopnji tehnološke proizvodnje oziroma proizvodnje znanja kot tudi na stopnji komercializacije. Ta pristop omogoča razlikovanje med državami, ki so učinkovite pri ustvarjanju rezultatov znanja, in tistimi, ki so uspešne pri pretvarjanju teh rezultatov v gospodarske učinke. Ugotovitve kažejo na znatne razlike v inovacijski učinkovitosti med državami članicami EU. Malo držav dosega visoko učinkovitost na obeh stopnjah, kar poudarja težavnost ohranjanja uspešnosti vzdolž celotne inovacijske vrednostne verige. Analiza razkriva trajne neučinkovitosti, zlasti na stopnji komercializacije, kar je skladno z dosedanjimi raziskavami, ki izpostavljajo strukturne ovire pri pretvarjanju rezultatov raziskav in razvoja v gospodarske koristi. Rezultati prav tako dokazujejo, da so razlike v razvrstitvah držav, ki jih opažamo v literaturi, pogosto posledica različnih izborov kazalnikov in specifikacij metode DEA. Tudi med študijami, ki uporabljajo podobne okvire DEA, razlike pri vključitvi kazalnikov vodijo do različnih razvrstitev uspešnosti držav.Praktične implikacije: metodološke odločitve omogočajo izboljšano primerljivost med državami in skozi čas, hkrati pa zmanjšujejo kompleksnost. Dvostopenjska struktura metode DEA oblikovalcem politik nudi globlji vpogled v notranje delovanje nacionalnih inovacijskih sistemov in vire neučinkovitosti, zlasti na stopnji komercializacije. To omogoča bolj ciljno usmerjene politične intervencije, ki razlikujejo med šibkostmi pri proizvodnji znanja in šibkostmi pri gospodarskem izkoriščanju inovacijskih rezultatov. Izvirnost/vrednost: študija prispeva k literaturi z uvedbo metode MCFS na področje ocenjevanja inovacijske učinkovitosti in ponuja poenostavljen, empirično utemeljen nabor kazalnikov, primeren za uporabo v okviru metode DEA. S kombinacijo podatkovno zasnovanega pristopa k izbiri spremenljivk in dvostopenjske metode DEA članek obravnava metodološko razdrobljenost v dosedanjih raziskavah ter zagotavlja preglednejši in ponovljiv okvir za vrednotenje nacionalnih inovacijskih sistemov"}],"edm:type":"TEXT","dc:type":[{"@xml:lang":"sl","#text":"znanstveno časopisje"},{"@xml:lang":"en","#text":"journals"},{"@rdf:resource":"http://www.wikidata.org/entity/Q361785"}]},"ore:Aggregation":{"@rdf:about":"http://www.dlib.si/?URN=URN:NBN:SI:doc-DSA91304","edm:aggregatedCHO":{"@rdf:resource":"URN:NBN:SI:doc-DSA91304"},"edm:isShownBy":{"@rdf:resource":"http://www.dlib.si/stream/URN:NBN:SI:doc-DSA91304/4955805d-835c-4e10-aba4-8084eccd0b73/PDF"},"edm:rights":{"@rdf:resource":"http://creativecommons.org/licenses/by-nc-nd/4.0/"},"edm:provider":"Slovenian National E-content Aggregator","edm:intermediateProvider":{"@xml:lang":"en","#text":"National and University Library of Slovenia"},"edm:dataProvider":{"@xml:lang":"sl","#text":"Univerza v Ljubljani, Fakulteta za upravo"},"edm:object":{"@rdf:resource":"http://www.dlib.si/streamdb/URN:NBN:SI:doc-DSA91304/maxi/edm"},"edm:isShownAt":{"@rdf:resource":"http://www.dlib.si/details/URN:NBN:SI:doc-DSA91304"}}}}