Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/37858
Title: The state of the art of discrete choice experiments in food research
Authors: LIZIN, Sebastien 
Rousseau, Sandra
Kessels, Roselinde
Meulders, Michel
PEPERMANS, Guido 
Speelman, Stijn
VANDEBROEK, Martina
Van Den Broeck, Goedele
Van Loo, Ellen J.
Verbeke, Wim
Issue Date: 2022
Publisher: ELSEVIER SCI LTD
Source: Food quality and preference, 102 (Art N° 104678)
Abstract: Discrete choice experiments (DCEs) have become an often-used research method in food research due to their ability to uncover trade-offs made when choosing among multiple alternatives, especially when dealing with credence attributes. Insights into the main elements of the consumers' decision-making process are key to informing both public and private policies related to food production and consumption. However, DCEs are not confined to this field of study. This narrative methodological review sets out to provide a critical appraisal of the state of the art of DCEs in food research. We logically structure our review by comparing the field-independent state-of-the-art to its application in the specific food choice research domain. The comparison is presented for each of the steps required in implementing DCEs and allows for the identification of areas of improvement in best practice. We find that food research has adopted many of the methodological advances over the years, but further improvements are encouraged and outlined. Recommendations for future research are discussed.
Notes: Lizin, S (corresponding author), Univ Hasselt, Martelarenlaan 42, B-3500 Hasselt, Belgium.
sebastien.lizin@uhasselt.be
Keywords: DCE;Food choice;Methodological review;Valuation;Best practice
Document URI: http://hdl.handle.net/1942/37858
ISSN: 0950-3293
e-ISSN: 1873-6343
DOI: 10.1016/j.foodqual.2022.104678
ISI #: WOS:000830841900004
Rights: 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
Category: A1
Type: Journal Contribution
Validations: ecoom 2023
Appears in Collections:Research publications

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