| Business-to-Consumer electronic auctions operate in a market environment where they compete with the online retailer offering to sell identical items to the consumers. To gain an understanding of and developing an operational strategy for such auctions poses a challenge in terms of the complexity involved in modeling the environment consisting of competing buyers on one hand and multiple sellers on the other. A two-stage research method is adopted to develop a comprehensive agent-based computational model. In the first stage, a theoretical revenue model is proposed based on the competitive interaction between the auction and retail markets by way of consumers' search for price-related information. In the second stage, following the empirical validation of theoretical model, bottom-up approach is used to develop an agent-based model incorporating all the elements of the environment i.e. the consumers, retailers, auctioneers.;The findings prove the relationship between the posted-prices in the retail market and the final winning bids in the B2C auction market, showing that the difference between the final bids and lowest retail price is attributable to the determinants of search costs, such as level of price dispersion, number of retailers carrying the product, etc. The agent-based model reveals that contrary to the popular belief of auction participants being gamblers, consumers in general are risk averse and rational, engaging in optimal search of retail market for price-related information when deciding on their willingness to pay. Additional experiments using the agent-based computational model demonstrate the flexibility of the approach and the richness it affords in terms of understanding the complex interactions and their effect on price-formation in the auctions. |
