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Consumers’ Preferences for Coffee Consumption: A Choice Experiment Integrated with Tasting and Chemical Analyses

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Models for Data Analysis (SIS 2018)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 402))

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Abstract

This study proposes an innovative approach for analysing consumer preferences for coffee by integrating a choice experiment with a guided tasting and chemical analysis. Firstly, two types of coffee were chosen from the mass market retailers with different sensorial profiles (100% Arabica, and Arabica and Robusta blends); subsequently, a guided tasting has been included to analyse the role of the sensory descriptors. An optimal design for the choice experiment was planned in order to achieve the joint purpose of the efficient estimation of the attributes, and the assessment of the information obtained from the guided tasting. The same choice experiment was administered twice, e.g. before and after the guided tasting. Random Utility Models were applied for better evaluating the consumers’ behaviour.

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Authors and Affiliations

  1. Department of Statistics Computer Science Applications “G. Parenti”, University of Florence, Viale Morgagni 59, Florence, Italy

    Rossella Berni, Nedka D. Nikiforova & Patrizia Pinelli

Authors
  1. Rossella Berni
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  2. Nedka D. Nikiforova
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  3. Patrizia Pinelli
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Corresponding author

Correspondence to Rossella Berni .

Editor information

Editors and Affiliations

  1. Department of Economics and Management, University of Brescia, Brescia, Italy

    Eugenio Brentari

  2. Department of Economics, Business and Statistics, University of Palermo, Palermo, Italy

    Marcello Chiodi

  3. Statistical Computing Laboratory, Faculty of Informatics, Università della Svizzera Italiana, Lugano, Switzerland

    Ernst-Jan Camiel Wit

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Berni, R., Nikiforova, N.D., Pinelli, P. (2023). Consumers’ Preferences for Coffee Consumption: A Choice Experiment Integrated with Tasting and Chemical Analyses. In: Brentari, E., Chiodi, M., Wit, EJ.C. (eds) Models for Data Analysis. SIS 2018. Springer Proceedings in Mathematics & Statistics, vol 402. Springer, Cham. https://doi.org/10.1007/978-3-031-15885-8_4

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