STUART MADNICK, Massachusetts Institute of Technology (MIT) - Sloan School of Management
Email: smadnick@mit.edu
MIHAI LUPU, affiliation not provided to SSRN
Email: lupelul@yahoo.com

The Context Interchange Strategy (COIN) is an approach to solving the problem of interoperability of semantically heterogeneous data sources through context mediation. The existing implementation of COIN uses its own notation and syntax for representing ontologies. More recently, the OWL Web Ontology Language is becoming established as the W3C recommended ontology language. A bridge is needed between these two areas and an explanation on how each of the two approaches can learn from each other. We propose the use of the COIN strategy to solve context disparity and ontology interoperability problems in the emerging Semantic Web both at the ontology level and at the data level. In this work we showcase how the problems that arise from context-dependent representation of facts can be mitigated by Semantic Web techniques, as tools of the conceptual framework developed over 15 years of COIN research.

THOMAS W. HAZLETT, George Mason University - School of Law
Email: thazlett@gmu.edu

Horizontal merger evaluation is heavily reliant on market definition. While a SSNIP framework formats the analysis, demand elasticity evidence used to apply the test is often sparse, as is often found in high-technology industries. This paper examines other sources of evidence that reveal the dynamics of market structure, data that are also probative in the evaluation of competitive effects. These sources include capital valuations of firms, financial event studies, and the public positions taken with respect to the merger by interested parties. Such evidence is examined in the XM-Sirius merger (2007-08), and shown - in two of the three instances - to be relatively informative in merger welfare analysis.

KENNETH C. WILBUR, University of Southern California - Marshall School of Business
Email: kwilbur@usc.edu
MICHELLE SOVINSKY GOEREE, Claremont Colleges - Claremont McKenna College
Email: michelle.goeree@claremontmckenna.edu
GEERT RIDDER, University of Southern California
Email: ridder@usc.edu

Digital video recorder proliferation and new commercial audience metrics are making television networks' revenues more sensitive to audience losses from advertising. There is currently limited understanding of how traditional advertising and product placement affect television audiences. We estimate a random coefficients logit model of viewing demand for television programs, wherein time given to traditional advertising and product placement plays a role akin to the "price" of consuming a program. Our data include audience, advertising, and program characteristics from more than 10,000 network-hours of prime-time broadcast television from 2004 to 2007. We find that the median effect of a 10% rise in traditional advertising time is a 15% reduction in audience size. We find evidence that creative strategy and product category factors are important determinants of viewer response to traditional advertising. When we control for program episode quality, we find that product placement time decreases viewer utility. In sum, our results imply that networks should give price discounts to those advertisers whose ads are most likely to retain viewers' interest throughout the commercial break.