Enhancing Online Auction Transaction Likelihood: A Comprehensive Data Mining Approach

Lei Chen and Yanbin Tu
Additional contact information
Lei Chen: Jianghan University, Hubei, China
Yanbin Tu: Robert Morris University, Pittsburgh, USA

International Journal of E-Business Research (IJEBR), 2019, vol. 15, issue 2, 116-132

Abstract: This article compares four data mining models (discriminant analysis, logistic regression, decision tree, and multilayer neural networks) for online auction transaction predictions. It aims to choose the best model in terms of prediction accuracy and to identify determinants significant for auction transactions. By using datasets from eBay, the authors find that the best data mining model for auction transactions is multilayer neural networks. Logistic regression and decision tree models can be used to identify determinants significant for auction transaction such as seller's feedback profile, listing picture, listing files size, return policies, and others. By adjusting these listing options, sellers could increase the auction transaction likelihood. This study will help sellers improve their auction listings by constructing effective selling strategies so that they can enhance the likelihood of online auction transactions. All these efforts will help improve their online auction performances and finally lead to a more efficient electronic marketplace.

Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJEBR.2019040107 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jebr00:v:15:y:2019:i:2:p:116-132

Access Statistics for this article

International Journal of E-Business Research (IJEBR) is currently edited by Mohammad Nabil Almunawar

More articles in International Journal of E-Business Research (IJEBR) from IGI Global
Bibliographic data for series maintained by Journal Editor ().