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The influence of attribute cutoffs on consumers' choices of a functional food

Author

Listed:
  • Yulian Ding
  • Michele M. Veeman
  • Wiktor L. Adamowicz

Abstract

Non-compensatory preferences are investigated by incorporating attribute cutoffs into models of consumer choices for food with health-related attributes (omega-3 content) that may be associated with genetic modification. We find empirical evidence that some individuals tend to use attribute cutoffs in decision-making, that incorporating these into the modelling of consumer choices significantly improves model fit, that some respondents are willing to take a utility penalty rather than eliminate an alternative when a cutoff violation occurs and that there is considerable heterogeneity in willingness to violate cutoffs. The study also provides some support to the hypothesis that ignoring cutoff endogeneity in model estimation may generate biased estimates. , Oxford University Press.

Suggested Citation

  • Yulian Ding & Michele M. Veeman & Wiktor L. Adamowicz, 2012. "The influence of attribute cutoffs on consumers' choices of a functional food," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 39(5), pages 745-769, December.
    • Handle: RePEc:oup:erevae:v:39:y:2012:i:5:p:745-769
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    File URL: http://hdl.handle.net/10.1093/erae/jbr067
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    Cited by:

    1. Zhang, Rong & Zhu, Lichao, 2019. "Threshold incorporating freight choice modeling for hinterland leg transportation chain of export containers," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 858-872.
    2. Peschel, Anne O. & Grebitus, Carola & Colson, Gregory & Hu, Wuyang, 2016. "Explaining the use of attribute cut-off values in decision making by means of involvement," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 65(C), pages 58-66.
    3. Kim, Kyungah & Kim, Jinseok & Park, Subin & Lee, Jongsu & Kim, Junghun, 2025. "A machine learning technique embedded reference-dependent choice model for explanatory power improvement: Shifting of reference point as a key factor in vehicle purchase decision-making," Transportation Research Part B: Methodological, Elsevier, vol. 191(C).
    4. Wiktor L. Adamowicz & Klaus Glenk & Jürgen Meyerhoff, 2014. "Choice modelling research in environmental and resource economics," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 27, pages 661-674, Edward Elgar Publishing.
    5. Gericke, Jens & Mehn, Audrey & Rommel, Kai, 2023. "Kundenpräferenzen für Bio-Lebensmittel in Deutschen Lebensmittelläden. Ergebnisse eines Discrete-Choice Experiments," Research Journal for Applied Management (RJAM), International School of Management (ISM), Dortmund, vol. 4(1), pages 42-64.
    6. Grebitus, Carola & Seitz, Carolin, 2014. "Relationship between attention and choice making," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 182669, European Association of Agricultural Economists.
    7. José Luis Espinosa-Aranda & Ricardo García-Ródenas & María Luz López-García & Eusebio Angulo, 2018. "Constrained nested logit model: formulation and estimation," Transportation, Springer, vol. 45(5), pages 1523-1557, September.
    8. Dubey, Subodh & Cats, Oded & Hoogendoorn, Serge & Bansal, Prateek, 2022. "A multinomial probit model with Choquet integral and attribute cut-offs," Transportation Research Part B: Methodological, Elsevier, vol. 158(C), pages 140-163.
    9. Moser, Riccarda & Raffaelli, Roberta, "undated". "Exploiting cut-off information to incorporate context effect: a discrete choice experiment on small fruits in a Alpine region," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114646, European Association of Agricultural Economists.
    10. Truong, Thuy D. & Adamowicz, Wiktor L. (Vic) & Boxall, Peter C., 2015. "Modeling non-compensatory preferences in environmental valuation," Resource and Energy Economics, Elsevier, vol. 39(C), pages 89-107.

    More about this item

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D1 - Microeconomics - - Household Behavior

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