A PROBABILISTIC DEMAND APPLICATION IN THE AMERICAN CRACKER MARKET

Knowledge of the distribution of consumer buying strategies by producers may permit improved marketing strategies and improved ability to respond to volatile market conditions. In order to investigate potential ways of gaining such knowledge, this study extends the work of Kahneman, Russell, and Thaler through the application of a probabilistic demand framework using Choice Wave theory, based on the Schrödinger Wave Equation in quantum mechanics. Probabilistic variability of response to health information and its potential influence on buying strategies is also investigated, extending the work of Clement, Johnson, Hu, Pagoulatos, and Debertin. In the present study, the domestic cracker market within fourteen U.S. metropolitan areas is segmented, using the Choice Wave Probabilistic Demand approach, into two statistically independent “Consumer Types” of seven metropolitan areas each. The two Consumer Types are shown to have statistically different elasticities than each other and from the combined market as a whole. This approach may provide not only improved marketing strategies through improved awareness of consumer preferences and buying strategies, especially in the volatile agricultural sector, but also may be useful in aiding producers of store brand/private label products in finding desirable markets in which to compete against national brands. The results also suggest that supply/producer-side strategies should take into account the ways in which information, whether under the direct control of the producer or not, may influence and change consumer buying strategies


Issue Date:
2016-07
Publication Type:
Journal Article
DOI and Other Identifiers:
ISSN 2147-8988 (Other)
E-ISSN: 2149-3766 (Other)
PURL Identifier:
http://purl.umn.edu/244386
Published in:
International Journal of Food and Agricultural Economics, Volume 04, Number 3
Page range:
49-61
Total Pages:
13
JEL Codes:
C15; C44; C61; D70; D81; N52
Series Statement:
Vol 4
No 3




 Record created 2017-04-01, last modified 2017-08-29

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