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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

What is the future of brand name beef? A price analysis of branding incentives and other attributes for retail beef using sales scanner data

White, Katharine L. January 1900 (has links)
Master of Science / Department of Agricultural Economics / Ted C. Schroeder / It is clear that consumers rely on certain experience and credence attributes when purchasing beef products from the retail meat case. It is essential for all beef industry sectors to recognize the complexity of consumers buying behavior. The objective of this research is to determine if there are incentives to brand beef products and to determine what types of brands entertain price premiums as well as what levels these premiums exists. Retail scanner data, collected from 2004 through March 2009, was used for the evaluation of branded beef and also to determine what other product attributes benefit with a premium to six specific cuts of beef. Hedonic models were estimated using Ordinary Least Squares regressions to determine which variables affected the overall price per pound of each of the six cuts of beef chosen to analyze. Results indicate that there is an incentive to brand beef products at the retail level. Local, regional, national, and store brands all garnered premiums across the six models for the beef cuts, steak, roast, ground, strip, cube, and ribs in relation to products with no brand. Other variables that garnered premiums across all models include organic, Prime quality grade, and Kosher and Kosher-Glatt religious labels. Steak exhibited the highest mean price per pound followed by cube, roast, strip, ribs and ground. In all of the models estimated explaining price variation, there were few coefficients that were statistically insignificant. Additional modeling was done to determine if outlier observations were influencing the regression results. The sensitivity analyses resulted in small changes in parameter estimates indicating the identified influential observations did not have undue impact on the parameter estimates.

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