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Consumer preferences for eggs using conjoint analysis

Published online by Cambridge University Press:  18 September 2007

Hubert Gerhardy  and
Mitchell R. Ness
Hubert Gerhardy
Affiliation:
Forschungs-und Studienzentrum für Veredelungswirtschaft Weser/Ems der Universität Göttingen, Driverstrasse 22, D-49377 Vechta, Germany
Mitchell R. Ness
Affiliation:
Department of Agricultural Economics and Food Marketing, University of Newcastle upon Tyne, NE1 7RU, UK
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Abstract

In the UK consumers are becoming more aware of issues related to food quality. Food marketers face the problem of responding to these developments by offering products which are consistent with changing consumer preferences. It is therefore increasingly important for marketers to understand the nature of consumers' preferences. This study focuses on the preferences of egg purchasers and uses conjoint analysis to identify consumer preference segments in the market. The analysis reveals that the preferences of consumers are very heterogeneous, but that it is possible to identify segments with distinct preferences for particular egg attributes.

Keywords

Conjoint analysisconsumerseggsmarketingpreferencesproduction systemsUK
Type
Research Article
Information
World's Poultry Science Journal , Volume 51 , Issue 2 , July 1955 , pp. 203 - 214
Copyright
Copyright © Cambridge University Press 1995

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References

Addelman, S. (1962) Orthogonal main effect plans for asymmetrical factorial experiments. Technometrics 1: 21–46CrossRefGoogle Scholar
Carmone, F. (1987) ACA system for adaptive conjoint analysis. Journal of Marketing Research 24: 325–327Google Scholar
Carmone, F.J., Green, P.E. and Jain, A.K. (1978) Robustness of conjoint analysis: some Monte Carlo results. Journal of Marketing Research 15: 300–303Google Scholar
Cattin, P. and Wittink, D.R. (1982) Commercial use of conjoint analysis: a survey. Journal of Marketing 46: 44–53CrossRefGoogle Scholar
Green, P.E. and Rao, V.R. (1971) Conjoint measurement of quantifying judgement data. Journal of Marketing Research 8: 355–363Google Scholar
Green, P.E., Krieger, A.M. and Bansal, P. (1988) Completely unacceptable levels in conjoint analysis: a cautionary note. Journal of Marketing Research 25: 293–300CrossRefGoogle Scholar
Green, P.E., Krieger, A.M. and Agarwal, M.K. (1991) Adaptive conjoint analysis: some caveats and suggestions. Journal of Marketing Research 28: 215–222CrossRefGoogle Scholar
Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (1992) Multivariate Data Analysis with Readings, 3rd edn, Macmillan Publishing, New YorkGoogle Scholar
Johnson, R.M. (1987) Adaptive conjoint analysis. Proceedings of Sawtooth Software Conference Perceptual Mapping Conjoint Analysis and Computer Interviewing, pp. 253–265Google Scholar
Johnson, R.M. (1991) Comment on ‘Adaptive conjoint analysis: some caveats and suggestions’. Journal of Marketing Research 28: 223–225Google Scholar
Maff (1993) Statistics: Eggs. Issue no.8/93.Google Scholar
Reibstein, D., Bateson, J.E.G. and Boulding, W. (1988) Conjoint analysis reliability: empirical findings. Marketing Science 7: 271–286CrossRefGoogle Scholar
Ritson, C. and Hutchins, R. (1992) Changing patterns of demand for food products in the UK. Proceedings of the International Seminar held in the Mediterranean Agronomic Institute of Chania,Greece,30 May 1992, pp. 31–54Google Scholar
Sarle, W.S. (1983) Cubic clustering criterion. In: SAS Technical Report A-108, SAS Institute Inc., Cary, North CarolinaGoogle Scholar
Sawtooth, (1992) ACA System, Version 3.1, Sawtooth Software, Inc.Google Scholar
Srinivasan, V. and Shocker, A.D. (1981) LINMAP Version IV-User's Manual, Vanderbilt University, Nashville, TennesseeGoogle Scholar
Wind, Y. (1978) Issues and advances in segmentation research. Journal of Marketing Research 15: 317–337CrossRefGoogle Scholar