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A Network Bidder Behavior Model in Online Auctions: A Case of Fine Art Auctions
Institution:1. Department of Marketing, Rawls College of Business, Texas Tech University, MS2101, Lubbock, TX 79409, United States;2. Department of Marketing, Lee Kong Chian School of Business, Singapore Management University, 50 Stamford Road #05-01, Singapore 178899, Singapore;3. Department of Marketing, Owen Graduate School of Management, Vanderbilt University, 401 21st Avenue South, Nashville, TN 37202, United States;1. Department of Management and Marketing, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;2. Department of Business Administration, College of Business and Public Policy, University of Alaska Anchorage, Anchorage, AK 99508, United States;1. Dongling School of Economics and Management, University of Science and Technology Beijing, China;2. School of Economics and Management, Tsinghua University, China
Abstract:The marketing literature provides a solid understanding of auctions regarding final sales prices and many aspects of the processes that unfold to result in those outcomes. This research complements those perspectives by first presenting a new bidder behavior model that shows the role of emergent network ties among bidders on the auction outcome. Dyadic ties are identified as the bid and counter-bid patterns of interactions between bidders that unfold throughout the duration of an auction. These structures are modeled using network analyses, which enables: (1) a richer understanding of detailed auction processes, both within auctions and across auctions of multiple lots, (2) a mapping of the processes to the forecast of prices and the trajectory toward final sales prices, (3) the clear and early identification of key bidders who are influential to the bidding action and who impact final auction sales prices, and (4) the results clearly show that the network exchange patterns are significant and contribute to an understanding of auction processes and outcomes above and beyond simple economic predictors such as the number of bids or bidders or the bidders’ economic status. We conclude by providing some managerial implications for online auction houses and bidders.
Keywords:Online auctions  Dynamic pricing  Bidders  Networks
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