This article attempts to examine the applicability of hedonic regression and conjoint analysis as a tool for supporting the pricing policy. Conjoint analysis and hedonic regression are both based on the assumption that durable consumer goods are conglomerates of significant and utility bearing attributes. Conjoint analysis measures partial utilities of characteristics of the commodity as components of the overall preference towards a certain variant of the product. Hedonic regression, in turn, allows for decomposition of the price of the commodity into set of prices of individual characteristics (called implicit or hedonic prices). It is expected that the combined use of these techniques will allow for broader insight into the issue of consumers' preferences and should provide sound basis for commodities pricing. Conducted empirical research was based on two complementary datasets relating tablet computers.
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.