Journal Article10.1300/J150V09N03_09
Online auctions: Dynamic pricing and the lodging industry
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TL;DR: Auction-based models impact business models by converting the price setting mechanism from supplier-centric to market-centric and transforming the trading model from one to many to many as mentioned in this paper.
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Abstract: The traditional channels of distribution for overnight accommodations are rapidly being displaced by website scripting, online intermediaries, and specialty brokers. Businesses that pioneered Internet usage relied on it as a sales and marketing alternative to predecessor product distribution channels. As such, websites replace the traditional trading model to the Internet. Web-enabled companies are popular because the medium renders the process faster, less costly, highly reliable, and secure. Auction-based models impact business models by converting the price setting mechanism from supplier-centric to market-centric and transforming the trading model from “one to many” to “many to many.” Historically, pricing was based on the cost of production plus a margin of profit. Traditionally, as products and services move through the supply chain, from the producer to the consumer, various intermediaries added their share of profit to the price. As Internet based mediums of distribution become more preva...
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Citations
Reserve price effects in auctions: estimates from multiple RD designs
TL;DR: In this article, the authors present evidence from 260,000 online auctions of second-hand cars to identify the impact of public reserve prices on auction outcomes, finding that an increase in reserve price decreases the number of bidders, increases the likelihood the object remains unsold, and increases expected revenue conditional on sale.
“Upping the ante”: how to design efficient auctions with entry?
TL;DR: In this paper, the authors formalize hold-up regarding entry costs that preys on second-price auctions when the seller may engage in a costly shill-bidding activity.
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Seeking in Ride-on-Demand Service: A Reinforcement Learning Model With Dynamic Price Prediction
Qianrong Shen,Baolin Deng,Chao Chen,Jintao Ke,Jingyuan Wang,Saiqin Long,Ke Xu +6 more
TL;DR: The paper proposes a reinforcement learning model for seeking route recommendation based on dynamic price prediction to increase driver revenue. The model utilizes multi-source urban data to predict dynamic prices and recommends highly profitable seeking routes to drivers of vacant cars.
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Online Auctions: A study of Bidder Satisfaction
Rupak Rauniar,John Simms,Deepak Rauniar +2 more
- 01 Jan 2009
TL;DR: In this paper, the authors extend past literature on user computing satisfaction to evaluate bidder satisfaction from the usage of online auction websites and find that a greater understanding of bidder satisfaction can facilitate the design of a customer-friendly user interface, thus increasing the effectiveness of auction websites.
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