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A dynamic personalised product pricing strategy using multiple attributes in agent mediated e-market - a neural approach

Author

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  • Neeraj Kumar Sharma
  • Vibha Gaur
  • Punam Bedi

Abstract

To attract buyers in the uncertain and distrusted environment of e-market, seller agents must use flexible and adaptive strategies. Being able to compute the right price of a good is vital for a seller agent to succeed in e-market that allows for prices to fluctuate due to uncertainty, different conditions, context and buyers' requirements. This paper addresses the problem of dynamically computing the appropriate selling price of a good for a prospective buyer, in response to the buyers' specifications for the goods' attributes in linguistic terms using artificial neural network in a competitive e-market. The proposed model helps in improving buyer-seller satisfaction by offering customised products to buyers where at the same time realising the expected revenue of sellers by enticing buyers to return in future transactions. It encourages trustworthy sharing of information among sellers by associating the concept of reputation among selling peers.

Suggested Citation

  • Neeraj Kumar Sharma & Vibha Gaur & Punam Bedi, 2014. "A dynamic personalised product pricing strategy using multiple attributes in agent mediated e-market - a neural approach," International Journal of Information and Decision Sciences, Inderscience Enterprises Ltd, vol. 6(1), pages 46-69.
    • Handle: RePEc:ids:ijidsc:v:6:y:2014:i:1:p:46-69
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