Paper Number
1804
Paper Type
Complete
Abstract
The paper studies the impact of bidding overtime in online art auctions. We identify two contrasting overtime bidding mechanisms influencing the final winning price: the positive effect of valuation learning and the negative effect of attention cost. We define a theoretical auction model with bidding mechanisms that yield an optimum outcome through Monte Carlo simulation. By evaluating four bidding overtime durations at six Chinese art auction houses, we consistently observe a positive effect on final prices with longer overtime policies in all settings, from which we validate the bidding mechanisms and propose an optimal design of overtime duration at 3 minutes. The paper then discusses whether these effects apply to lower-value eBay auctions and physical auctions.
Recommended Citation
Tang, Hanyang and Zhai, Yingda, "Optimal Bidding Overtime Strategy for Online Art Auctions: Evidence from Chinese Auction Houses" (2024). ICIS 2024 Proceedings. 13.
https://aisel.aisnet.org/icis2024/digital_comm/digital_comm/13
Optimal Bidding Overtime Strategy for Online Art Auctions: Evidence from Chinese Auction Houses
The paper studies the impact of bidding overtime in online art auctions. We identify two contrasting overtime bidding mechanisms influencing the final winning price: the positive effect of valuation learning and the negative effect of attention cost. We define a theoretical auction model with bidding mechanisms that yield an optimum outcome through Monte Carlo simulation. By evaluating four bidding overtime durations at six Chinese art auction houses, we consistently observe a positive effect on final prices with longer overtime policies in all settings, from which we validate the bidding mechanisms and propose an optimal design of overtime duration at 3 minutes. The paper then discusses whether these effects apply to lower-value eBay auctions and physical auctions.
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