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Predicting Consumer Preferences for Fresh Salmon: The Influence of Safety Inspection and Production Method Attributes

Published online by Cambridge University Press:  15 September 2016

Daniel Holland  and
Cathy R. Wessells
Daniel Holland
Affiliation:
Department of Environmental and Natural Resource Economics, University of Rhode Island, Kingston, R.I.
Cathy R. Wessells
Affiliation:
Department of Environmental and Natural Resource Economics, University of Rhode Island, Kingston, R.I.
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Abstract

A rank-ordered logit model is estimated using data collected by a mail survey of consumers in the northeastern and mid-Atlantic United States. The methodology, based on conjoint analysis, determines the average relative importance and value of three product attributes for fresh salmon (seafood inspection, production method, and price), and estimates the relative attractiveness of particular products to consumers. When used in combination with demographic data and responses to questions on perceptions, the analysis suggests market segmentations and potential marketing strategies based on the heterogeneity in preferences among consumers.

Type
Articles
Information
Agricultural and Resource Economics Review , Volume 27 , Issue 1 , April 1998 , pp. 1 - 14
Copyright
Copyright © 1998 Northeastern Agricultural and Resource Economics Association 

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References

Addleman, S. 1962. “Orthogonal Main-Effect Plans for Asymmetrical Factorial Experiments.” Technometrics 4(1): 21–46.CrossRefGoogle Scholar
Anderson, J.L., and Bettencourt, S.U. 1993. “A Conjoint Approach to Model Product Preferences: The New England Market for Fresh and Frozen Salmon.” Marine Resource Economics 8: 31–49.CrossRefGoogle Scholar
Beggs, S., Cardell, S., and Hausman, J. 1981. “Assessing the Potential Demand for Electric Cars.” Journal of Econometrics 17: 1–19.CrossRefGoogle Scholar
Cohen, J., and Cohen, P. 1975. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Hillsdale, N.J.: Lawrence Earlbaum Associates, Inc.Google Scholar
Desvousges, W., Smith, V.K., and McGivney, M. 1983. “A Comparison of Alternative Approaches for Estimation of Recreation and Related Benefits of Water Quality Improvements.” Report no. EPA-230-05-83-001. Washington, D.C.: U.S. Environmental Protection Agency.Google Scholar
Elrod, T., Louviere, J.J., and Davey, K.S. 1992. “An Empirical Comparison of Ratings-Based and Choice-Based Conjoint Models.” Journal of Marketing Research 29: 368–77.CrossRefGoogle Scholar
Green, P.E., and Helsen, K. 1989. “Cross-Validation Assessment to Individual-Level Conjoint Analysis: A Case Study.” Journal of Marketing Research 26: 346–50.CrossRefGoogle Scholar
Green, P.E., and Srinivasan, V. 1978. “Conjoint Analysis in Consumer Research: Issues and Outlook.” Journal of Consumer Research 5: 103–23.Google Scholar
Green, P.E., and Srinivasan, V. 1990. “Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice.” Journal of Marketing 54(4): 3–19.CrossRefGoogle Scholar
Johnson, H.M. 1996. 1996 Annual Report on the United States Seafood Industry. Bellevue, Wash.Google Scholar
Levin, I.P., Louviere, J.J., Schepanski, A.A. and Norman, K.L. 1983. “External Validity Tests of Laboratory Studies of Information Integration.” Organizational Behavior and Human Performance 31: 173–93.CrossRefGoogle Scholar
Louviere, J.J. 1988. “Conjoint Analysis Modeling of Stated Preferences.” Journal of Transport Economics and Policy 22(1): 93–119.Google Scholar
MacKenzie, J. 1990. “Conjoint Analysis of Deer Hunting.” Northeastern Journal of Agricultural and Resource Economics 19: 109–17.CrossRefGoogle Scholar
MacKenzie, J. 1993. “A Comparison of Contingent Preference Models.” American Journal of Agricultural Economics 75: 593–603.CrossRefGoogle Scholar
Manalo, A.B. 1990. “Assessing the Importance of Apple Attributes: An Agricultural Application of Conjoint Analysis.” Northern Journal of Agricultural and Resource Economics 19: 118–124.Google Scholar
Moore, W.L. 1980. “Levels of Aggregation in Conjoint Analysis: An Empirical Comparison.” Journal of Marketing Research 17: 516–23.CrossRefGoogle Scholar
Rae, D.A. 1983. “The Value to Visitors of Improving Visibility at Mesa Verde and Great Smoky National Parks.” In Managing Air Quality and Scenic Resources at National Parks and Wilderness Areas, ed. Rowe, R.D. and Chestnut, L.G., 217–34. Boulder: Westview Press.Google Scholar
Stoker, T.M. 1993. “Empirical Approaches to the Problem of Aggregation Over Individuals.” Journal of Economic Literature 31: 1827–74.Google Scholar
Swallow, S.K., Weaver, T.W., Opaluch, J.J., and Michelman, T.S. 1994. “Heterogeneous Preferences and Aggregation in Environmental Policy Analysis: A Landfill Siting Case.” American Journal of Agricultural Economics 76: 431–43.Google Scholar
Vatn, A., and Bromley, D. 1994. “Choices without Prices without Apologies.” Journal of Environmental Economics and Management 26(2): 129–48.CrossRefGoogle Scholar
Wirth, F.F., Halbrendt, C.K., and Vaughn, G.F. 1991. “Conjoint Analysis of the Mid-Atlantic Food-Fish Market for Farm-raised Hybrid Striped Bass.” Southern Journal of Agricultural Economics 23: 155–64.Google Scholar
Wittink, D.R., and Cattin, P. 1981. “Alternative Estimation Methods for Conjoint Analysis: A Monte Carlo Study.” Journal of Marketing Research 18: 101–6.Google Scholar