Title: Effective selling strategies for online auctions on eBay: a comprehensive approach with CART model

Authors: Yanbin Tu; Y. Alex Tung; Paulo Goes

Addresses: Department of Marketing, School of Business, Robert Morris University, Moon Township, PA 15108, USA; School of Business, Jianghan University, Wuhan, Hubei 430056, China ' Department of Operations and Information Management, School of Business, University of Connecticut, Storrs, CT 06269, USA ' Department of Management Information Systems, Eller College of Management, University of Arizona, Tucson, AZ 85721, USA

Abstract: Most existing studies on selling strategies in online auctions do not distinguish auction heterogeneity when providing operational selling recommendations. They also tend to assume single objective for sellers. In this study, we incorporate seller and product heterogeneity into our analytical framework and implement data mining analysis in four auction segments. We use classification and regression tree (CART) to identify the critical factors along with their sequences for auction success and prices. We find different determinants for auction success and ending prices in these four auction segments. The classification and regression trees provide operational choices for sellers to build the most effective selling strategies. We propose that, by using expected auction prices with the classification and regression trees, sellers can integrate auction success and prices as multiple objectives in their selling strategies. Overall, this study contributes to the literature by providing an innovative methodology for effective selling recommendations, which can potentially lead to significant and smooth growth of the online auction market.

Keywords: online auctions; electronic marketplaces; data mining; selling strategies; classification; regression.

DOI: 10.1504/IJBIS.2019.097532

International Journal of Business Information Systems, 2019 Vol.30 No.2, pp.125 - 151

Received: 18 Jun 2016
Accepted: 07 May 2017
Published online: 28 Jan 2019 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article