<?xml version="1.0"?><rdf: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-HJ5JM1ZV/9233d263-fd4f-412e-ba1f-7ffb10c77b51/PDF"><dcterms:extent>1264 KB</dcterms:extent></edm:WebResource><edm:WebResource rdf:about="http://www.dlib.si/stream/URN:NBN:SI:DOC-HJ5JM1ZV/c7e4891b-adc3-4685-a3f4-ad2cc42656a2/TEXT"><dcterms:extent>0 KB</dcterms:extent></edm:WebResource><edm:TimeSpan rdf:about="2014-2025"><edm:begin xml:lang="en">2014</edm:begin><edm:end xml:lang="en">2025</edm:end></edm:TimeSpan><edm:ProvidedCHO rdf:about="URN:NBN:SI:DOC-HJ5JM1ZV"><dcterms:isPartOf rdf:resource="https://www.dlib.si/details/URN:NBN:SI:spr-QCV9XF2O" /><dcterms:issued>2025</dcterms:issued><dc:creator>Chen, J. L.</dc:creator><dc:creator>Lin, S. Y.</dc:creator><dc:creator>Zheng, M. Y.</dc:creator><dc:format xml:lang="sl">letnik:20</dc:format><dc:format xml:lang="sl">številka:4</dc:format><dc:format xml:lang="sl">str. 475-490</dc:format><dc:identifier>DOI:10.14743/apem2025.4.553</dc:identifier><dc:identifier>ISSN:1854-6250</dc:identifier><dc:identifier>COBISSID_HOST:265931523</dc:identifier><dc:identifier>URN:URN:NBN:SI:doc-HJ5JM1ZV</dc:identifier><dc:language>en</dc:language><dc:publisher xml:lang="sl">Fakulteta za strojništvo, Inštitut za proizvodno strojništvo</dc:publisher><dcterms:isPartOf xml:lang="sl">Advances in production engineering and management</dcterms:isPartOf><dc:subject xml:lang="en">AI delivery</dc:subject><dc:subject xml:lang="en">artificial intelligence</dc:subject><dc:subject xml:lang="en">consumer behaviour</dc:subject><dc:subject xml:lang="sl">državne spodbude</dc:subject><dc:subject xml:lang="en">government incentives</dc:subject><dc:subject xml:lang="en">logistics delivery services</dc:subject><dc:subject xml:lang="en">logistics enterprise decision-making</dc:subject><dc:subject xml:lang="en">technology adoption</dc:subject><dc:subject xml:lang="en">tripartite evolutionary game mode</dc:subject><dc:subject xml:lang="sl">umetna inteligenca</dc:subject><dc:subject xml:lang="sl">vedenje potrošnikov</dc:subject><dcterms:temporal rdf:resource="2014-2025" /><dc:title xml:lang="sl">Tripartite evolutionary game analysis of the adoption of AI delivery technology|</dc:title><dc:description xml:lang="sl">The decision of logistics enterprises to adopt AI delivery technology is influenced by multiple stakeholders, a fact that is largely ignored in existing research. A tripartite evolutionary game model incorporating the government, logistics enterprises, and consumers was established in this study. It analyzed the strategies adopted by the three actors in promoting AI delivery technology and examined the factors influencing their choices. Furthermore, the evolutionary equilibrium and stability of these strategies were explored and verified through simulation analysis. Results reveal several key insights: (1) Technology promotion is a collaborative process driven by multiple stakeholders. Government subsidies, enterprise costs and benefits, and consumer utility are the crucial variables determining system stability. (2) Government incentives not only reduce enterprises’ adoption costs but also increase consumer willingness to adopt through subsidies and publicity. These measures accelerate the system’s evolution toward the ideal stable state of “introduction, incentive, AI delivery”. (3) Enterprises’ investment decisions are highly sensitive to input costs and economic benefits. The threshold for technology adoption decreases when significant economic benefits are present or costs decline. (4) Consumers’ perceived utility and learning costs directly affect their usage intentions and subsequently influence the strategic choices of the government and enterprises through demand feedback. This study provides a novel perspective on the promotion of AI delivery and offers practical management insights for policy-making and user analysis in logistics enterprises</dc:description><edm:type>TEXT</edm:type><dc:type xml:lang="sl">znanstveno časopisje</dc:type><dc:type xml:lang="en">journals</dc:type><dc:type rdf:resource="http://www.wikidata.org/entity/Q361785" /></edm:ProvidedCHO><ore:Aggregation rdf:about="http://www.dlib.si/?URN=URN:NBN:SI:DOC-HJ5JM1ZV"><edm:aggregatedCHO rdf:resource="URN:NBN:SI:DOC-HJ5JM1ZV" /><edm:isShownBy rdf:resource="http://www.dlib.si/stream/URN:NBN:SI:DOC-HJ5JM1ZV/9233d263-fd4f-412e-ba1f-7ffb10c77b51/PDF" /><edm:rights rdf:resource="http://creativecommons.org/licenses/by/4.0/" /><edm:provider>Slovenian National E-content Aggregator</edm:provider><edm:intermediateProvider xml:lang="en">National and University Library of Slovenia</edm:intermediateProvider><edm:dataProvider xml:lang="sl">Univerza v Mariboru, Fakulteta za strojništvo</edm:dataProvider><edm:object rdf:resource="http://www.dlib.si/streamdb/URN:NBN:SI:DOC-HJ5JM1ZV/maxi/edm" /><edm:isShownAt rdf:resource="http://www.dlib.si/details/URN:NBN:SI:DOC-HJ5JM1ZV" /></ore:Aggregation></rdf:RDF>