{"?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-4TZPOGUK/7ca9c19b-20df-4479-b0bd-b82fd82c3a67/PDF","dcterms:extent":"439 KB"},{"@rdf:about":"http://www.dlib.si/stream/URN:NBN:SI:DOC-4TZPOGUK/eb12f157-8f85-4e97-b542-08e9e5116383/TEXT","dcterms:extent":"0 KB"}],"edm:TimeSpan":{"@rdf:about":"2018-2025","edm:begin":{"@xml:lang":"en","#text":"2018"},"edm:end":{"@xml:lang":"en","#text":"2025"}},"edm:ProvidedCHO":{"@rdf:about":"URN:NBN:SI:DOC-4TZPOGUK","dcterms:isPartOf":[{"@rdf:resource":"https://www.dlib.si/details/URN:NBN:SI:spr-3NZXSA6F"},{"@xml:lang":"sl","#text":"Central European Public Administration Review"}],"dcterms:issued":"2025","dc:creator":["Lazović-Pita, Lejla","Odžak, Almasa","Šćeta, Lamija"],"dc:format":[{"@xml:lang":"sl","#text":"številka:1"},{"@xml:lang":"sl","#text":"letnik:23"},{"@xml:lang":"sl","#text":"str. 129-155"}],"dc:identifier":["DOI:10.17573/cepar.2025.1.05","COBISSID_HOST:237538051","ISSN:2591-2240","URN:URN:NBN:SI:doc-4TZPOGUK"],"dc:language":"en","dc:publisher":{"@xml:lang":"sl","#text":"= University of Ljubljana Press"},"dc:subject":[{"@xml:lang":"en","#text":"corruption"},{"@xml:lang":"en","#text":"EVS"},{"@xml:lang":"en","#text":"individual tax morale"},{"@xml:lang":"sl","#text":"individualna davčna morala"},{"@xml:lang":"sl","#text":"korupcija"},{"@xml:lang":"en","#text":"machine learning"},{"@xml:lang":"sl","#text":"strojno učenje"}],"dcterms:temporal":{"@rdf:resource":"2018-2025"},"dc:title":{"@xml:lang":"sl","#text":"Examining individual tax morale in Europe with machine-learning methods|"},"dc:description":[{"@xml:lang":"sl","#text":"Purpose: This research examines and contributes to the behavioural literature on voluntary tax compliance. It focuses on the use and potential of machine-learning (ML) methods and models to predict individual tax morale across Europe, and it identifies the factors that influence predictive accuracy. Design/Methodology/Approach: Using data from the fifth wave (2017–2020) of the European Values Survey (EVS), a data-driven, systematic approach employing six ML methods is applied to predict individual tax morale across Europe. The importance of formal, informal and socio-demographic factors is assessed, and the study tests whether incorporating the Corruption Perception Index (CPI) improves predictive accuracy. Findings: The results indicate that ML methods and models can enhance understanding and prediction of individual tax morale in Europe. Among the deployed models, artificial neural networks (ANNs) achieved the highest accuracy. Accuracy increased across all ML methods when the CPI was included. Attitudes towards bribery, perceptions of immigrants’ impact on the national welfare system, and gender emerged as significant formal, informal and socio-demographic factors. Academic contribution to the field: The study offers a novel application of data-driven ML methods to the prediction of individual tax morale. Given the scarcity of empirical ML research in the social sciences, the findings provide valuable insights in a European context and may serve as a basis for further global research. Practical Implications: The conclusions are particularly relevant for governments and tax administrations seeking to improve tax compliance and revenue collection. In the European context, the results confirm the virtuous circle linking effective government performance, high tax morale and voluntary tax compliance—insights that are crucial for decision-makers, regulators, European institutions and tax-policy makers. Originality/Value: The findings confirm that, when ML methods are applied, individual tax morale can be viewed as an outcome of interactions between formal and informal institutions. They also show that predictive accuracy is higher in countries with lower corruption, as indicated by a higher CPI"},{"@xml:lang":"sl","#text":"Namen: Raziskava proučuje in nadgrajuje vedenjsko literaturo o prostovoljnem izpolnjevanju davčnih obveznosti. Osredotoča se na uporabo in potencial metod ter modelov strojnega učenja za napoved individualne davčne morale po Evropi ter opredeljuje dejavnike, ki vplivajo na napovedno natančnost. Zasnova/metodologija/pristop: Na podlagi podatkov petega vala (2017–2020) Evropske raziskave vrednot (EVS) je bil uporabljen podatkovno voden, sistematičen pristop, ki vključuje šest metod strojnega učenja za napovedovanje individualne davčne morale v Evropi. Ocenjena je bila pomembnost formalnih, neformalnih in soci-demografskih dejavnikov, hkrati pa je študija preverila, ali vključitev Indeksa zaznave korupcije (Corruption Perception Index – CPI) poveča napovedno natančnost. Ugotovitve: Rezultati kažejo, da lahko metode in modeli strojnega učenja izboljšajo razumevanje ter napovedovanje individualne davčne morale v Evropi. Med uporabljenimi modeli so umetne nevronske mreže dosegle najvišjo natančnost. Pri vseh metodah strojnega učenja se je natančnost povečala, ko je bil v model vključen CPI. Odnos do podkupovanja, zaznave vpliva priseljencev na nacionalni socialni sistem in spol so se izkazali za pomembne formalne, neformalne in soci-demografske dejavnike. Akademski prispevek k področju: Študija je nov, podatkovno usmerjen pristop uporabe metod strojnega učenja za napovedovanje individualne davčne morale. Zaradi redkosti empiričnih raziskav strojnega učenja v družboslovju ugotovitve ponujajo dragocene vpoglede v evropskem kontekstu in so lahko temelj za nadaljnje globalne raziskave. Praktična uporabnost: Sklepi so posebej pomembni za vlade in davčne uprave, ki želijo izboljšati davčno skladnost in stopnjo pobiranja prihodkov. V evropskem kontekstu rezultati potrjujejo krog pozitivnih povratnih zank, ki povezuje učinkovito delovanje vlade, visoko davčno moralo in prostovoljno izpolnjevanje davčnih obveznosti. Gre za spoznanja, ki so ključna za odločevalce, regulatorje, evropske institucije in oblikovalce davčne politike. Izvirnost/vrednost: Ugotovitve potrjujejo, da lahko pri uporabi metod strojnega učenja individualno davčno moralo obravnavamo kot rezultat interakcij med formalnimi in neformalnimi institucijami. Prav tako kažejo, da je napovedna natančnost večja v državah z nižjo stopnjo korupcije, kar se kaže v višjem CPI"}],"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-4TZPOGUK","edm:aggregatedCHO":{"@rdf:resource":"URN:NBN:SI:DOC-4TZPOGUK"},"edm:isShownBy":{"@rdf:resource":"http://www.dlib.si/stream/URN:NBN:SI:DOC-4TZPOGUK/7ca9c19b-20df-4479-b0bd-b82fd82c3a67/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-4TZPOGUK/maxi/edm"},"edm:isShownAt":{"@rdf:resource":"http://www.dlib.si/details/URN:NBN:SI:DOC-4TZPOGUK"}}}}