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

Neeraj Kumar Sharma, Vibha Gaur and Punam Bedi

International Journal of Information and Decision Sciences, 2014, vol. 6, issue 1, 46-69

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.

Keywords: dynamic product pricing; selling price; back-propagation; artificial neural networks; ANNs; fuzzy attributes; reputation; personalised products; pricing strategy; mediated e-markets; electronic markets; online markets; customised products; trustworthiness. (search for similar items in EconPapers)
Date: 2014
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