IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i14p3934-d249772.html
   My bibliography  Save this article

Identifying Drivers of Genetically Modified Seafood Demand: Evidence from a Choice Experiment

Author

Listed:
  • Michael J. Weir

    (Department of Environmental and Natural Resource Economics, University of Rhode Island, Kingston, RI 02881, USA)

  • Thomas W. Sproul

    (Department of Environmental and Natural Resource Economics, University of Rhode Island, Kingston, RI 02881, USA)

Abstract

The aquaculture industry has expanded to fill the gap between plateauing wild seafood supply and growing consumer seafood demand. The use of genetic modification (GM) technology has been proposed to address sustainability concerns associated with current aquaculture practices, but GM seafood has proved controversial among both industry stakeholders and producers, especially with forthcoming GM disclosure requirements for food products in the United States. We conduct a choice experiment eliciting willingness-to-pay for salmon fillets with varying characteristics, including GM technology and GM feed. We then develop a predictive model of consumer choice using LASSO (least absolute shrinkage and selection operator)-regularization applied to a mixed logit, incorporating risk perception, ambiguity preference, and other behavioral measures as potential predictors. Our findings show that health and environmental risk perceptions, confidence and concern about potential health and environmental risks, subjective knowledge, and ambiguity aversion in the domain of GM foods are all significant predictors of salmon fillet choice. These results have important implications for marketing of foods utilizing novel food technologies. In particular, people familiar with GM technology are more likely to be open to consuming GM seafood or GM-fed seafood, and effective information interventions for consumers will include details about health and environmental risks associated with GM seafood.

Suggested Citation

  • Michael J. Weir & Thomas W. Sproul, 2019. "Identifying Drivers of Genetically Modified Seafood Demand: Evidence from a Choice Experiment," Sustainability, MDPI, vol. 11(14), pages 1-21, July.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:14:p:3934-:d:249772
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/14/3934/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/11/14/3934/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. George Gaskell & Nick Allum & Wolfgang Wagner & Nicole Kronberger & Helge Torgersen & Juergen Hampel & Julie Bardes, 2004. "GM Foods and the Misperception of Risk Perception," Risk Analysis, John Wiley & Sons, vol. 24(1), pages 185-194, February.
    2. Gülbanu Kaptan & Arnout R.H. Fischer & Lynn J. Frewer, 2018. "Extrapolating understanding of food risk perceptions to emerging food safety cases," Journal of Risk Research, Taylor & Francis Journals, vol. 21(8), pages 996-1018, August.
    3. Arthur Snow, 2010. "Ambiguity and the value of information," Journal of Risk and Uncertainty, Springer, vol. 40(2), pages 133-145, April.
    4. Emily Oster, 2018. "Diabetes and Diet: Purchasing Behavior Change in Response to Health Information," American Economic Journal: Applied Economics, American Economic Association, vol. 10(4), pages 308-348, October.
    5. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2011. "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls," Papers 1201.0224, arXiv.org, revised May 2012.
    6. Lynn J. Frewer & Chaya Howard & Duncan Hedderley & Richard Shepherd, 1997. "The Elaboration Likelihood Model and Communication About Food Risks," Risk Analysis, John Wiley & Sons, vol. 17(6), pages 759-770, December.
    7. Viscusi, W Kip, 1997. "Alarmist Decisions with Divergent Risk Information," Economic Journal, Royal Economic Society, vol. 107(445), pages 1657-1670, November.
    8. Paul Slovic, 1999. "Trust, Emotion, Sex, Politics, and Science: Surveying the Risk‐Assessment Battlefield," Risk Analysis, John Wiley & Sons, vol. 19(4), pages 689-701, August.
    9. Shiping Liu & Ju‐Chin Huang & Gregory L. Brown, 1998. "Information and Risk Perception: A Dynamic Adjustment Process," Risk Analysis, John Wiley & Sons, vol. 18(6), pages 689-699, December.
    10. Joseph K. Goodman & Gabriele Paolacci, 2017. "Crowdsourcing Consumer Research," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 44(1), pages 196-210.
    11. Mizerski, Richard W, 1982. "An Attribution Explanation of the Disproportionate Influence of Unfavorable Information," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 9(3), pages 301-310, December.
    12. Olivier Bonroy & Christos Constantatos, 2015. "On the Economics of Labels: How Their Introduction Affects the Functioning of Markets and the Welfare of All Participants," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(1), pages 239-259.
    13. W. Kip Viscusi & Wesley A. Magat & Joel Huber, 1999. "Smoking Status and Public Responses to Ambiguous Scientific Risk Evidence," Southern Economic Journal, John Wiley & Sons, vol. 66(2), pages 250-270, October.
    14. Daniel Kahneman & Jack L. Knetsch & Richard H. Thaler, 1991. "Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias," Journal of Economic Perspectives, American Economic Association, vol. 5(1), pages 193-206, Winter.
    15. Stefan Hut & Emily Oster, 2018. "Changes in Household Diet: Determinants and Predictability," NBER Working Papers 24892, National Bureau of Economic Research, Inc.
    16. George Loewenstein, 2000. "Emotions in Economic Theory and Economic Behavior," American Economic Review, American Economic Association, vol. 90(2), pages 426-432, May.
    17. M. Hino & E. Benami & N. Brooks, 2018. "Machine learning for environmental monitoring," Nature Sustainability, Nature, vol. 1(10), pages 583-588, October.
    18. Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ziad Obermeyer, 2015. "Prediction Policy Problems," American Economic Review, American Economic Association, vol. 105(5), pages 491-495, May.
    19. Lusk, Jayson L. & Rozan, Anne, 2008. "Public Policy and Endogenous Beliefs: The Case of Genetically Modified Food," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 33(2), pages 1-20.
    20. Jayson L. Lusk & Keith H. Coble, 2005. "Risk Perceptions, Risk Preference, and Acceptance of Risky Food," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(2), pages 393-405.
    21. Kivi, Paul A. & Shogren, Jason F., 2010. "Second-Order Ambiguity in Very Low Probability Risks: Food Safety Valuation," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 35(3), pages 1-14, December.
    22. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    23. Craig R. Fox & Amos Tversky, 1995. "Ambiguity Aversion and Comparative Ignorance," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(3), pages 585-603.
    24. Petrolia, Daniel R., 2016. "Risk preferences, risk perceptions, and risky food," Food Policy, Elsevier, vol. 64(C), pages 37-48.
    25. Mas-Colell, Andreu & Whinston, Michael D. & Green, Jerry R., 1995. "Microeconomic Theory," OUP Catalogue, Oxford University Press, number 9780195102680.
    26. Dan M. Kahan & Hank Jenkins-Smith & Donald Braman, 2011. "Cultural cognition of scientific consensus," Journal of Risk Research, Taylor & Francis Journals, vol. 14(2), pages 147-174, February.
    27. Steenkamp, Jan-Benedict E. M., 1990. "Conceptual model of the quality perception process," Journal of Business Research, Elsevier, vol. 21(4), pages 309-333, December.
    28. Jong-Min Kim & Hojin Jung, 2019. "Predicting bid prices by using machine learning methods," Applied Economics, Taylor & Francis Journals, vol. 51(19), pages 2011-2018, April.
    29. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    30. Andini, Monica & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Salvestrini, Viola, 2018. "Targeting with machine learning: An application to a tax rebate program in Italy," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 86-102.
    31. Samir Huseynov & Bachir Kassas & Michelle S. Segovia & Marco A. Palma, 2019. "Incorporating biometric data in models of consumer choice," Applied Economics, Taylor & Francis Journals, vol. 51(14), pages 1514-1531, March.
    32. Stephen Dimmock & Roy Kouwenberg & Olivia Mitchell & Kim Peijnenburg, 2015. "Estimating ambiguity preferences and perceptions in multiple prior models: Evidence from the field," Journal of Risk and Uncertainty, Springer, vol. 51(3), pages 219-244, December.
    33. George Gaskell & Katrin Hohl & Monica M. Gerber, 2017. "Do closed survey questions overestimate public perceptions of food risks?," Journal of Risk Research, Taylor & Francis Journals, vol. 20(8), pages 1038-1052, August.
    34. Heath, Chip & Tversky, Amos, 1991. "Preference and Belief: Ambiguity and Competence in Choice under Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 4(1), pages 5-28, January.
    35. Lusk, Jayson L. & Roosen, Jutta & Shogren, Jason (ed.), 2011. "The Oxford Handbook of the Economics of Food Consumption and Policy," OUP Catalogue, Oxford University Press, number 9780199569441.
    36. Ryan J. Tibshirani & Jonathan Taylor & Richard Lockhart & Robert Tibshirani, 2016. "Exact Post-Selection Inference for Sequential Regression Procedures," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 600-620, April.
    37. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "Supplementary Appendix for "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls"," Papers 1305.6099, arXiv.org, revised Jun 2013.
    38. Hoch, Stephen J & Ha, Young-Won, 1986. "Consumer Learning: Advertising and the Ambiguity of Product Experience," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 13(2), pages 221-233, September.
    39. Kent D. Messer & Marco Costanigro & Harry M. Kaiser, 2017. "Labeling Food Processes: The Good, the Bad and the Ugly," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 39(3), pages 407-427.
    40. Curtis, Kynda R. & Wahl, Thomas I. & McCluskey, Jill J., 2003. "Consumer Acceptance of Genetically Modified Food Products in the Developing World," 2003 Conference (47th), February 12-14, 2003, Fremantle, Australia 57858, Australian Agricultural and Resource Economics Society.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Smeele, Nicholas V.R. & Chorus, Caspar G. & Schermer, Maartje H.N. & de Bekker-Grob, Esther W., 2023. "Towards machine learning for moral choice analysis in health economics: A literature review and research agenda," Social Science & Medicine, Elsevier, vol. 326(C).
    2. Chloe S McCallum & Simone Cerroni & Daniel Derbyshire & W George Hutchinson & Rodolfo M Nayga, 2022. "Consumers’ responses to food fraud risks: an economic experiment," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 49(4), pages 942-969.
    3. Zheng, Qiujie & Nayga, Rodolfo M. Jr. & Yang, Wei & Tokunaga, Kanae, 2022. "Do U.S. consumers value genetically modified farmed salmon?," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322154, Agricultural and Applied Economics Association.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. McKenzie, David & Sansone, Dario, 2017. "Man vs. Machine in Predicting Successful Entrepreneurs: Evidence from a Business Plan Competition in Nigeria," CEPR Discussion Papers 12523, C.E.P.R. Discussion Papers.
    2. Andini, Monica & Boldrini, Michela & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Paladini, Andrea, 2022. "Machine learning in the service of policy targeting: The case of public credit guarantees," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 434-475.
    3. Shaw, W. Douglass & Woodward, Richard T., 2008. "Why environmental and resource economists should care about non-expected utility models," Resource and Energy Economics, Elsevier, vol. 30(1), pages 66-89, January.
    4. Costanigro, Marco & Scozzafava, Gabriele & Casini, Leonardo, 2019. "Vertical differentiation via multi-tier geographical indications and the consumer perception of quality: The case of Chianti wines," Food Policy, Elsevier, vol. 83(C), pages 246-259.
    5. Yash Raj Shrestha & Vivianna Fang He & Phanish Puranam & Georg von Krogh, 2021. "Algorithm Supported Induction for Building Theory: How Can We Use Prediction Models to Theorize?," Organization Science, INFORMS, vol. 32(3), pages 856-880, May.
    6. Mercè Roca & Robin Hogarth & A. Maule, 2006. "Ambiguity seeking as a result of the status quo bias," Journal of Risk and Uncertainty, Springer, vol. 32(3), pages 175-194, May.
    7. Bryan T. Kelly & Asaf Manela & Alan Moreira, 2019. "Text Selection," NBER Working Papers 26517, National Bureau of Economic Research, Inc.
    8. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "High-Dimensional Methods and Inference on Structural and Treatment Effects," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 29-50, Spring.
    9. John Griffin, 2015. "Risk Premia and Knightian Uncertainty in an Experimental Market Featuring a Long-Lived Asset," Fordham Economics Discussion Paper Series dp2015-01, Fordham University, Department of Economics.
    10. Frick, Mira & Iijima, Ryota & Le Yaouanq, Yves, 2019. "Boolean Representations of Preferences under Ambiguity," Rationality and Competition Discussion Paper Series 173, CRC TRR 190 Rationality and Competition.
    11. Peter D. Lunn, 2013. "Telecommunications Consumers: A Behavioral Economic Analysis," Journal of Consumer Affairs, Wiley Blackwell, vol. 47(1), pages 167-189, April.
    12. repec:cup:judgdm:v:2:y:2007:i::p:390-397 is not listed on IDEAS
    13. Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023. "Big data forecasting of South African inflation," Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
    14. Laure Cabantous & Denis Hilton & Howard Kunreuther & Erwann Michel-Kerjan, 2011. "Is imprecise knowledge better than conflicting expertise? Evidence from insurers’ decisions in the United States," Journal of Risk and Uncertainty, Springer, vol. 42(3), pages 211-232, June.
    15. David Hirshleifer, 2001. "Investor Psychology and Asset Pricing," Journal of Finance, American Finance Association, vol. 56(4), pages 1533-1597, August.
    16. Michael C. Knaus & Michael Lechner & Anthony Strittmatter, 2022. "Heterogeneous Employment Effects of Job Search Programs: A Machine Learning Approach," Journal of Human Resources, University of Wisconsin Press, vol. 57(2), pages 597-636.
    17. Chen, Ya & Tsionas, Mike G. & Zelenyuk, Valentin, 2021. "LASSO+DEA for small and big wide data," Omega, Elsevier, vol. 102(C).
    18. L. Robin Keller & Rakesh K. Sarin & Jayavel Sounderpandian, 2007. "An examination of ambiguity aversion: Are two heads better than one?," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 2, pages 390-397, December.
    19. Elisa Cavatorta & David Schröder, 2019. "Measuring ambiguity preferences: A new ambiguity preference survey module," Journal of Risk and Uncertainty, Springer, vol. 58(1), pages 71-100, February.
    20. Astrid Gamba & Anna Bottasso, 2019. "Consumer inertia in energy markets: Insights from behavioral economics," ECONOMIA PUBBLICA, FrancoAngeli Editore, vol. 2019(3), pages 113-130.
    21. de Blasio, Guido & D'Ignazio, Alessio & Letta, Marco, 2022. "Gotham city. Predicting ‘corrupted’ municipalities with machine learning," Technological Forecasting and Social Change, Elsevier, vol. 184(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:14:p:3934-:d:249772. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.