Is online product information availability driven by quality or differentiation?
AuthorMarkopoulos, Panos M.
Ungar, Lyle H.
PublisherAffiliation: The Wharton School, University of Pennsylvania, Philadelphia, PA, United States
Correspondence Address: Markopoulos, P. M.
The Wharton School, University of Pennsylvania, Philadelphia, PA, United States
SourceAssociation for Information Systems - 26th International Conference on Information Systems, ICIS 2005: Forever New Frontiers
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We present a game theoretic model for the availability of product information in Internet markets, where buyers can search for multiple products in parallel. We use a multiple-circle variation of Salop's "unit circle" model of product differentiation where vendors are able to differentiate their products both horizontally (taste) and vertically (quality). We explore the conditions under which vendors make horizontal and vertical product information available to potential customers in equilibrium. We demonstrate that vendors will choose not to provide their full horizontal product information, and will rather leave the buyers with some probabilistic knowledge about their exact horizontal product locations. However, the vendors will release enough horizontal product information for their products to appear distinct from those of competitors. The sellers' incentives to disseminate vertical product information are shown to be fundamentally different: only the worst possible quality vendors will withhold information on vertical product parameters. Our results suggest an answer for the question that is the title of this paper: Is it the case that online vendors release product information primarily to advertise their product's superiority or to make clear that their products do not have close competing substitutes? We find that for high quality products the former is more important while the latter gains significance for lower quality products. We present empirical observations of nearly 2,000 products in the PC game industry that provide evidence in favor of the model's predictions.