Title page for ETD etd-09202005-140727


Type of Document Dissertation
Author Hertweck, Bryan M.
Author's Email Address bhertwec@highpoint.edu
URN etd-09202005-140727
Title Examining Electronic Markets in Which Intelligent Agents Are Used for Comparison Shopping and Dynamic Pricing
Degree PhD
Department Management Science and Information Technology
Advisory Committee
Advisor Name Title
Rakes, Terry R. Committee Chair
Brown, Evelyn C. Committee Member
Matheson, Lance Arthur Committee Member
Rees, Loren Paul Committee Member
Zobel, Christopher W. Committee Member
Keywords
  • Neural Networks
  • Intelligent Agents
  • Electronic Commerce
  • Simulation
Date of Defense 2005-09-08
Availability unrestricted
Abstract

Electronic commerce markets are becoming increasingly popular forums for commerce. As those markets mature, buyers and sellers will both vigorously seek techniques to improve their performance. The Internet lends itself to the use of agents to work on behalf of buyers and sellers. Through simulation, this research examines different implementations of buyers' agents (shopbots) and sellers' agents (pricebots) so that buyers, sellers, and agent builders can capitalize on the evolution of e-commerce technologies.

Internet markets bring price visibility to a level beyond what is observed in traditional brick-and-mortar markets. Additionally, an online seller is able to update prices quickly and cheaply. Due to these facts, there are many pricing strategies that sellers can implement via pricebot to react to their environments. The best strategy for a particular seller is dependent on characteristics of its marketplace. This research shows that the extent to which buyers are using shopbots is a critical driver of the success of pricing strategies. When measuring profitability, the interaction between shopbot usage and seller strategy is very strong - what works well at low shopbot usage levels may perform poorly at high levels. If a seller is evaluating strategies based on sales volume, the choice may change. Additionally, as markets evolve and competitors change strategies, the choice of most effective counterstrategies may evolve as well. Sellers need to clearly define their goals and thoroughly understand their marketplace before choosing a pricing strategy.

Just as sellers have choices to make in implementing pricebots, buyers have decisions to make with shopbots. In addition to the factors described above, the types of shopbots in use can actually affect the relative performance of pricing strategies. This research also shows that varying shopbot implementations (specifically involving the use of a price memory component) can affect the prices that buyers ultimately pay - an especially important consideration for high-volume buyers.

Modern technology permits software agents to employ artificial intelligence. This work demonstrates the potential of neural networks as a tool for pricebots. As discussed above, a seller's best strategy option can change as the behavior of the competition changes. Simulation can be used to evaluate a multitude of scenarios and determine what strategies work best under what conditions. This research shows that a neural network can be effectively implemented to classify the behavior of competitors and point to the best counterstrategy.

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