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Oyster Demand Adjustments to Counter-Information and Source Treatments in Response to Vibrio vulnificus

Published online by Cambridge University Press:  26 January 2015

O. Ashton Morgan
Affiliation:
Department of Economics, Appalachian State University, Boone, NC
Gregory S. Martin
Affiliation:
Department of Marketing, Northern Kentucky University, Highland Heights, KY
William L. Huth
Affiliation:
Department of Marketing and Economics, University of West Florida, Pensacola, FL

Abstract

A web-based contingent behavior analysis was developed to quantify the effect of both negative and positive information treatments and post harvest processes on demand for oysters. Results from a panel model indicate that consumers of raw and cooked oysters behave differently after news of an oyster-related human mortality. While cooked oyster consumers take precautionary measures against risk, raw oyster consumers exhibit optimistic bias and increase their consumption level. Further, by varying the source of a counter-information treatment, we find that source credibility impacts behavior. Oyster consumers, and in particular, raw oyster consumers, are most responsive to information provided by a not-for-profit, nongovernmental organization. Finally, post harvest processing of oysters has no impact on demand.

Type
Research Article
Copyright
Copyright © Southern Agricultural Economics Association 2009

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References

Agresti, A. Categorical Data Analysis. New York: John Wiley & Sons, 1990.Google Scholar
Armor, D.A., and Taylor., S.E.Situated Optimism: Specific Outcome Expectancies and Self-regulation.Advances in Experimental Social Psychology 30(1998):309–79.Google Scholar
Brown, D.J., and Schrader, L.F.Cholesterol Information and Shell Egg Consumption.American Journal of Agricultural Economics 72(1990):548–55.Google Scholar
Corcoran, L.Raw Oysters: Deadly Delicacy.” Nutritional Action Healthletter, 1998. Internet site: http://findarticles.corn/p/articles/mi_m0813/is_n8_v25/ai_21245202/ (Accessed September 21, 2008).Google Scholar
Crano, W.Effect of Sex, Response Order, and Expertise in Conformity: A Dispositional Approach.Sociometry 33(1970):239–52.Google Scholar
Creel, M.D., and Loomis., J.B.Confidence Intervals for Welfare Measures with Application to a Problem of Truncated Counts.The Review of Economics and Statistics 73(1990):370–73.Google Scholar
Dahlgran, R.A., and Fairchild., D.G.The Demand Impacts of Chicken Contamination Publicity - A Case Study.Agribusiness 18(2002):459–74.Google Scholar
Egan, K., and Herriges., J.Multivariate Count Data Regression Models with Individual Data from an On-site Sample.Journal of Environmental Economics and Management 52(2006): 567–81.Google Scholar
Flattery, J., and Bashin., M. A Baseline Survey of Raw Oyster Consumers in Four States. International Shellfish Sanitation Conference 2003. Internet site: http://www.issc.org/Vibrio_vulnificus_Education/Baseline%20Survey.pdf (Accessed April 15, 2008).Google Scholar
Florida Department of Agriculture and Consumer Services. Division of Aquaculture. Florida Vibrio vulnificus Risk Reduction Plan for Oysters 2005. Internet site: http://www.floridaaquaculture.com/publications/VVriskreduction.pdf (Accessed December 10, 2008).Google Scholar
Haab, T.C., and McConnell., K.E. Valuing Environmental and Natural Resources: The Econometrics of Non-market Valuation. Northampton, MA: Edward Elgar, 2002.CrossRefGoogle Scholar
Hanson, T.L., House, L., Sureshwaren, S., Posadas, B., and Liu., A. Opinions of U.S. Consumers Toward Oysters: Results of a 2000–2001 Survey. U.S. Department of Agriculture, Agreement #99–38614-8202, Mississippi Alabama Sea Grant Consortium, 2003. Internet site: http://www.aquanic.org/species/shellfish/docu-mentsZbll33.pdf (Accessed September 17, 2007).Google Scholar
Hellerstein, D.Can We Count on Count Models.” Valuing Recreation and the Environment: Revealed Preference Methods in Theory and Practice. Herriges, J.A. and Kling, C.L., eds. Cheltenham, UK: Edward Elgar, 1999.Google Scholar
Hovland, C.L., and Weiss., W.The Influence of Source Credibility on Communication Effectiveness.Public Opinion Quarterly 15(1951): 635–50.Google Scholar
Huffman, W.E., Rousu, M., Shogren, J.F., and Tegene., A.Who Do Consumers Trust for Information: The Case of Genetically Modified Foods?American Journal of Agricultural Economics 86(2004): 1222–29.CrossRefGoogle Scholar
Huffman, W.E., and Tegene., A.Public Acceptance of and Benefits from Agricultural Biotechnology: A Key Role for Verifiable Information.” Market Development for Genetically Modified Food. Santaniello, V., Evenson, R.E., and Zilberman, D., eds. New York: CAB International, 2002.Google Scholar
Johnson, H., and Steiner., I.The Effects of Source on Response to Negative Information about One's Self.The Journal of Social Psychology 74(1968):215–24.Google Scholar
Johnston, R.J., Wessells, C.R., Donath, H., and Asche., F.Measuring Consumer Preferences for Ecolabeled Seafood: An International Comparison.Journal of Agricultural and Resource Economics 26(2001):20—39.Google Scholar
List, J.A., and Gallet., C.A.What Experimental Protocol Influence Disparities Between Actual and Hypothetical Stated Values?Environmental and Resource Economics 20(2001): 241–54.Google Scholar
Miles, S., and Frewer., L.J.Investigating Specific Concerns about Different Food Hazards.Food Quality and Preference 12(2001):4761.CrossRefGoogle Scholar
Miles, S., and Scaife., V.Optimistic Bias and Food.Nutrition Research Reviews 16(2003): 319.Google Scholar
Milgrom, P., and Roberts., J.Relying on the Information of Interested Parties.The Rand Journal of Economics 17(1986): 1832.Google Scholar
Murphy, J.J., Allen, P.G., Stevens, T.H., and Weatherhead., D.A Meta Analysis of Hypothetical Bias in Stated Preference Valuation.Environmental and Resource Economics 30(2005):313–25.Google Scholar
Parsons, G.R., Morgan, O.A., Whitehead, J.C., and Haab., T.C.The Welfare Effects of Pfiesteria-Related Fish Kills in Seafood Markets: A Contingent Behavior Analysis.Agricultural and Resource Economics Review 35(2006): 19.Google Scholar
Piggot, N.E., and Marsh., T.L.Does Food Safety Information Impact U.S. Meat Demand?American Journal of Agricultural Economics 86(2004): 151–74.Google Scholar
Shepherd, R.Social Determinants of Food Choice.The Proceedings of the Nutrition Society 58(1999):807–12.Google Scholar
Shulstad, R.N., and Stoevener., H.H.The Effects of Mercury Contamination in Pheasants on the Value of Pheasant Hunting in Oregon.Land Economics 54(1978):3949.CrossRefGoogle Scholar
Smith, E., van Ravenswaay, E.O., and Thompson., S.R.Sales Loss Determination in Food Contamination Incidents: An Application to Milk Bans in Hawaii.American Journal of Agricultural Economics 70(1988):513—20.Google Scholar
Sparks, P., and Shepherd., R.Public Perceptions of the Potential Hazards Associated with Food Production and Food Consumption: An Empirical Study.Risk Analysis 14(1994):799806.Google Scholar
Sternthal, B.P., Lynn, W., and Dholakia., R.The Persuasive Effect of Source Credibility: A Situational Analysis.Public Opinion Quarterly 41(1978):285314.Google Scholar
Swartz, D.G., and Strand., I.E.Avoidance Costs Associated with Imperfect Information: The Case of Kepone.Land Economics 57(1981): 139–50.Google Scholar
Taylor, S.E., and Brown., J.D.Illusion and Well-being: A Social Psychological Perspective on Mental Health.Psychological Bulletin 103(1988): 193210.Google Scholar
Weinstein, N.D., and Klein., W.M.Resistance of Personal Risk Perceptions to Debiasing Interventions.Health Psychology 14(1995): 132–40.Google Scholar
Wessells, C.R., and Anderson., J.G.Consumer Willingness to Pay for Seafood Safety Assurances.The Journal of Consumer Affairs 29(1995):85107.Google Scholar
Whitehead, J.C.Environmental Risk and Averting Behavior: Predictive Validity of Jointly Estimated Revealed and Stated Behavior Data.Environmental and Resource Economics 32(2005):301–16.Google Scholar