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Valuing Consumer Preferences with the CUB Model: A Case Study of Fairtrade Coffee


  • Cicia, Gianni
  • Corduas, Marcella
  • Del Giudice, Teresa
  • Piccolo, Domenico


In recent years, in the field of consumer behaviour, a large number of new models and instruments for preference analysis have been proposed. This strand of the literature has developed along two different lines. The first has produced approaches that have a more solid economic basis, but which at the same time require increasingly complex econometric analysis. Moreover, in this research field, based on stochastic utility theory and choice experiments, less weight is given to the socio-economic and psychometric characteristics of the individual in determining preferences. By contrast, the second strand has given rise to many methods to analyse consumer behaviour based on quality approaches such as laddering or focus groups where behavioural characteristics and lifestyles have regained primary importance in explaining the choices and “tastes” of individuals.

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  • Cicia, Gianni & Corduas, Marcella & Del Giudice, Teresa & Piccolo, Domenico, 2009. "Valuing Consumer Preferences with the CUB Model: A Case Study of Fairtrade Coffee," 2009 International European Forum, February 15-20, 2009, Innsbruck-Igls, Austria 59209, International European Forum on System Dynamics and Innovation in Food Networks.
  • Handle: RePEc:ags:iefi09:59209

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    References listed on IDEAS

    1. D'Elia, Angela & Piccolo, Domenico, 2005. "A mixture model for preferences data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 49(3), pages 917-934, June.
    2. Ferrini, Silvia & Scarpa, Riccardo, 2007. "Designs with a priori information for nonmarket valuation with choice experiments: A Monte Carlo study," Journal of Environmental Economics and Management, Elsevier, vol. 53(3), pages 342-363, May.
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    Cited by:

    1. Hellberg-Bahr, Anneke & Pfeuffer, Martin & Spiller, Achim & Brümmer, Bernhard, 2011. "Using Price Rigidities to Explain Pricing Strategies in the Organic Milk Chain," 2011 International European Forum, February 14-18, 2011, Innsbruck-Igls, Austria 122003, International European Forum on Innovation and System Dynamics in Food Networks.
    2. Rotaris Lucia & Danielis Romeo, 2011. "Willingness to Pay for Fair Trade Coffee: A Conjoint Analysis Experiment with Italian Consumers," Journal of Agricultural & Food Industrial Organization, De Gruyter, vol. 9(1), pages 1-22, June.
    3. Arboretti Giancristofaro, Rosa & Bordignon, Paolo, 2015. "Consumer preferences in food packaging: cub models and conjoint analysis," 143rd Joint EAAE/AAEA Seminar, March 25-27, 2015, Naples, Italy 202707, European Association of Agricultural Economists.
    4. Van Loo, Ellen J. & Caputo, Vincenzina & Nayga, Rodolfo M. & Seo, Han-Seok & Zhang, Baoyue & Verbeke, Wim, 2015. "Sustainability labels on coffee: Consumer preferences, willingness-to-pay and visual attention to attributes," Ecological Economics, Elsevier, vol. 118(C), pages 215-225.
    5. Marcella Corduas & Alfonso Piscitelli, 2017. "Modeling university student satisfaction: the case of the humanities and social studies degree programs," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 617-628, March.
    6. Federica Cugnata & Silvia Salini, 2014. "Model-based approach for importance–performance analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3053-3064, November.
    7. Corduas, Marcella, 2015. "A statistical model for consumer preferences: the case of Italian extra virgin olive oil," 143rd Joint EAAE/AAEA Seminar, March 25-27, 2015, Naples, Italy 202701, European Association of Agricultural Economists.
    8. Maria Iannario & Marica Manisera & Domenico Piccolo & Paola Zuccolotto, 2012. "Sensory analysis in the food industry as a tool for marketing decisions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(4), pages 303-321, December.

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    Agribusiness; Agricultural and Food Policy; Farm Management; Food Consumption/Nutrition/Food Safety; Research Methods/ Statistical Methods;

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