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Towards Virtual 3D Asset Price Prediction Based on Machine Learning

Information and Communication Management, Department of Economics and Management, Technische Universität Berlin, 10623 Berlin, Germany
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Academic Editor: Eduardo Álvarez-Miranda
J. Theor. Appl. Electron. Commer. Res. 2022, 17(3), 924-948; https://doi.org/10.3390/jtaer17030048
Received: 24 May 2022 / Revised: 28 June 2022 / Accepted: 4 July 2022 / Published: 7 July 2022
(This article belongs to the Section e-Commerce Analytics)
Although 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. View Full-Text
Keywords: 3D model; virtual asset; virtual product; virtual good; pricing; machine learning; feature scoring; e-commerce; metaverse 3D model; virtual asset; virtual product; virtual good; pricing; machine learning; feature scoring; e-commerce; metaverse
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MDPI and ACS Style

Korbel, J.J.; Siddiq, U.H.; Zarnekow, R. Towards Virtual 3D Asset Price Prediction Based on Machine Learning. J. Theor. Appl. Electron. Commer. Res. 2022, 17, 924-948. https://doi.org/10.3390/jtaer17030048

AMA Style

Korbel JJ, Siddiq UH, Zarnekow R. Towards Virtual 3D Asset Price Prediction Based on Machine Learning. Journal of Theoretical and Applied Electronic Commerce Research. 2022; 17(3):924-948. https://doi.org/10.3390/jtaer17030048

Chicago/Turabian Style

Korbel, Jakob J., Umar H. Siddiq, and Rüdiger Zarnekow. 2022. "Towards Virtual 3D Asset Price Prediction Based on Machine Learning" Journal of Theoretical and Applied Electronic Commerce Research 17, no. 3: 924-948. https://doi.org/10.3390/jtaer17030048

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