Estimating consumers' willingness to pay for individual quality attributes with DEA
Jeong-Dong Lee (),
A Repkine (),
Hwang S-W and
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A Repkine: Techno-Economics and Policy Program, College of Engineering, Seoul National University
Hwang S-W: Techno-Economics and Policy Program, College of Engineering, Seoul National University
Kim T-Y: Techno-Economics and Policy Program, College of Engineering, Seoul National University
Journal of the Operational Research Society, 2004, vol. 55, issue 10, 1064-1070
Abstract In a highly competitive environment, a product's commercial success depends increasingly more upon the ability to satisfy consumers' preferences that are highly diversified. Since a product typically comprises a host of technological attributes, its market value incorporates all of the individual values of technological attributes. If the willingness-to-pay (WTP) for individual quality attributes of a product is known, one can conjecture the overall WTP or the imputed market price for the product. The market price listed by the producer has to be equal to or lower than this WTP for the commercial survival of the product. In this paper, we propose a methodology for estimating the value of individual product characteristics and thus the overall WTP of the product with DEA. Our methodology is based on a model derived from consumer demand theory on the one hand, and the recent developments in DEA on the other hand. The paper also presents a real case study for the mobile phone market, which is characterized by its high speed of innovation. On the theoretical side, we expect our framework to provide a possibility of combining DEA and consumer demand theory. We also expect that the empirical application will shed some light on the nature of the process of product differentiation based on consumers' valuation.
Keywords: DEA; efficient consumption; willingness to pay; multi-attribute product pricing (search for similar items in EconPapers)
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Working Paper: Estimating Consumers’ Willingness to Pay for the Individual Quality Attributes with DEA (2004)
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