Online markets have enjoyed explosive growths and emerged as an important research topic in the field of electronic commerce. Researchers have mostly focused on studying consumer behavior and experience, while largely neglecting the seller side of these markets.
Our research addresses the problem of examining strategies sellers employ in listing their products on online market places. In particular, we introduce a Markov Chain model that captures and predicts seller listing behavior based on their present and past actions, their relative positions in the market, and market conditions. These features distinguish our approach from existing models that usually overlook the importance of historical information, as well as sellers’ interactions.
We choose to examine successful sellers on eBay, one of the most prominent online marketplaces, and empirically test our model framework using eBay’s data for fixed-priced items collected over a period of four and a half months.
This empirical study entails comparing our most complex history-dependent model’s predictive power against that of a semi-random behavior baseline model and our own history-independent model. The outcomes exhibit differences between different sellers in their listing strategies for different products, and validate our models’ capability in capturing seller behavior. Furthermore, the incorporation of historical information on seller actions in our model proves to improve its predictions of future behavior