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Consumer preferences for eggs using conjoint analysis

Published online by Cambridge University Press:  18 September 2007

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.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1995

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References

Addelman, S. (1962) Orthogonal main effect plans for asymmetrical factorial experiments. Technometrics 1: 2146CrossRefGoogle Scholar
Carmone, F. (1987) ACA system for adaptive conjoint analysis. Journal of Marketing Research 24: 325327Google 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: 300303Google Scholar
Cattin, P. and Wittink, D.R. (1982) Commercial use of conjoint analysis: a survey. Journal of Marketing 46: 4453CrossRefGoogle Scholar
Green, P.E. and Rao, V.R. (1971) Conjoint measurement of quantifying judgement data. Journal of Marketing Research 8: 355363Google 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: 293300CrossRefGoogle Scholar
Green, P.E., Krieger, A.M. and Agarwal, M.K. (1991) Adaptive conjoint analysis: some caveats and suggestions. Journal of Marketing Research 28: 215222CrossRefGoogle 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: 223225Google 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: 271286CrossRefGoogle 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: 317337CrossRefGoogle Scholar