Towards Virtual 3D Asset Price Prediction Based on Machine Learning

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dc.contributor.authorKorbel, Jakob J.
dc.contributor.authorSiddiq, Umar H.
dc.contributor.authorZarnekow, RĂ¼diger
dc.date.accessioned2022-10-11T06:35:03Z
dc.date.available2022-10-11T06:35:03Z
dc.date.issued2022-07-07
dc.date.updated2022-09-03T18:18:34Z
dc.description.abstractAlthough 3D models are today indispensable in various industries, the adequate pricing of 3D models traded on online platforms, i.e., virtual 3D assets, remains vague. This study identifies relevant price determinants of virtual 3D assets through the analysis of a dataset containing the characteristics of 135.384 3D models. Machine learning algorithms were applied to derive a virtual 3D asset price prediction tool based on the analysis results. The evaluation revealed that the random forest regression model is the most promising model to predict virtual 3D asset prices. Furthermore, the findings imply that the geometry and number of material files, as well as the quality of textures, are the most relevant price determinants, whereas animations and file formats play a minor role. However, the analysis also showed that the pricing behavior is still substantially influenced by the subjective assessment of virtual 3D asset creators.
dc.description.sponsorshipDFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berlin
dc.identifier.eissn0718-1876
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/17564
dc.identifier.urihttps://doi.org/10.14279/depositonce-16345
dc.language.isoen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc330 Wirtschaftde
dc.subject.other3D model
dc.subject.othervirtual asset
dc.subject.othervirtual product
dc.subject.othervirtual good
dc.subject.otherpricing
dc.subject.othermachine learning
dc.subject.otherfeature scoring
dc.subject.othere-commerce
dc.subject.othermetaverse
dc.titleTowards Virtual 3D Asset Price Prediction Based on Machine Learning
dc.typeArticle
dc.type.versionpublishedVersion
dcterms.bibliographicCitation.doi10.3390/jtaer17030048
dcterms.bibliographicCitation.issue3
dcterms.bibliographicCitation.journaltitleJournal of Theoretical and Applied Electronic Commerce Research
dcterms.bibliographicCitation.originalpublishernameMDPI
dcterms.bibliographicCitation.originalpublisherplaceBasel
dcterms.bibliographicCitation.pageend948
dcterms.bibliographicCitation.pagestart924
dcterms.bibliographicCitation.volume17
tub.accessrights.dnbfree
tub.affiliationFak. 7 Wirtschaft und Management::Inst. Technologie und Management (ITM)::FG Informations- und Kommunikationsmanagement (IKM)
tub.publisher.universityorinstitutionTechnische Universität Berlin

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