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Testing consumer perception of nutrient content claims using conjoint analysis

  • Adam Drewnowski (a1), Howard Moskowitz (a2), Michele Reisner (a2) and Bert Krieger (a2)

The US Food and Drug Administration (FDA) proposes to establish standardized and mandatory criteria upon which front-of-pack (FOP) nutrition labelling must be based. The present study aimed to estimate the relative contribution of declared amounts of different nutrients to the perception of the overall ‘healthfulness’ of foods by the consumer.


Protein, fibre, vitamin A, vitamin C, calcium and iron were nutrients to encourage. Total fat, saturated fat, cholesterol, total and added sugar, and sodium were the nutrients to limit. Two content claims per nutrient used the FDA-approved language. An online consumer panel (n 320) exposed to multiple messages (n 48) rated the healthfulness of each hypothetical food product. Utility functions were constructed using conjoint analysis, based on multiple logistic regression and maximum likelihood estimation.


Consumer perception of healthfulness was most strongly driven by the declared presence of protein, fibre, calcium and vitamin C and by the declared total absence of saturated fat and sodium. For this adult panel, total and added sugar had lower utilities and contributed less to the perception of healthfulness. There were major differences between women and men.


Conjoint analysis can lead to a better understanding of how consumers process information about the full nutrition profile of a product, and is a powerful tool for the testing of nutrient content claims. Such studies can help the FDA develop science-based criteria for nutrient profiling that underlies FOP and shelf labelling.

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1. Drewnowski A & Fulgoni F (2008) Nutrient profiling of foods: creating a nutrient-rich food index. Nutr Rev 66, 2339.
2.Smart Choices Program™ (2009) Helping Guide Smart Food and Beverage Choices. (accessed December 2009).
3. Drewnowski A (2005) Concept of a nutritious food: toward a nutrient density score. Am J Clin Nutr 82, 721732.
4.Food and Drug Administration (2008) Experimental Study of Nutrition Symbols on Food Packages. (accessed December 2009).
5.Food and Drug Administration (2008) Guidance for Industry: A Food Labeling Guide. (accessed December 2009).
6. Borra S (2006) Consumer perspectives on food labels. Am J Clin Nutr 83, 1235S.
7. Krieger B, Cappuccio R & Moskowitz H (2003) Next generation healthy soup: an exploration using conjoint analysis. J Sens Stud 18, 249268.
8.Food and Drug Administration (2009) [Docket No. 2007N-0277] Food Labeling: Use of Symbols to Communicate Nutrition Information, Consideration of Consumer Studies and Nutritional Criteria. (accessed December 2009).
9.US Department of Agriculture (2005) Steps to a Healthier You. (accessed September 2009).
10. Moskowitz H, Gofman A, Itty B et al. (2001) Rapid, inexpensive, actionable concept generation and optimization: the use and promise of self-authoring conjoint analysis for the food service industry. Food Service Technol 1, 149167.
11. Box GEP, Hunter WG & Hunter JS (1978) Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building. New York: Wiley.
12. Gofman A (2006) Emergent scenarios, synergies and suppressions uncovered within conjoint analysis. J Sens Stud 21, 373414.
13. Bosnjak M & Tuten TL (2001) Classifying response behaviors in web-based surveys. J Comput Mediat Commun 6, issue 3; available at
14. Fulgoni VL 3rd, Keast DR & Drewnowski A (2009) Development and validation of the nutrient-rich foods index: a tool to measure nutritional quality of foods. J Nutr 139, 15491554.
15. Darmon N, Vieux F, Maillot M et al. (2009) Nutrient profiles discriminate between foods according to their contribution to nutritionally adequate diets: a validation study using linear programming and the SAIN,LIM system. Am J Clin Nutr 89, 12271236.
16. Drichoutis AC, Lazaridis P & Nayga RM Jr (2006) Consumers’ use of nutritional labels: a review of research studies and issues. Acad Mark Sci Rev 2006, issue 9; available at
17. Ippolito PM & Mathios AD (1993) New food labeling regulations and the flow of nutrition information to consumers. J Public Policy Mark 12, 188205.
18. Cowburn G & Stockley L (2005) Consumer understanding and use of nutrition labelling: a systematic review. Public Health Nutr 8, 2128.
19. Drichoutis AC & Lazaridis P (2005) Nutrition knowledge and consumer use of nutritional food labels. Eur Rev Agric Econ 32, 93118.
20. Kim S-Y, Nayga RM Jr & Capps O Jr (2001) Health knowledge and consumer use of nutrition labels: the issue revisited. Agric Resource Econ Rev 30, 1019.
21. Burton S & Andrews JC (1996) Age, product nutrition, and label format effects on consumer perceptions and product evaluations. J Consum Aff 30, 6989.
22. Nayga RJ (2000) Nutrition knowledge, gender, and food label use. J Consum Aff 34, 97112.
23. Andrews JC, Netemeyer RG & Burton S (1998) Consumer Generalization of Nutrient Content Claims in Advertising. Cambridge, MA: Marketing Science Institute.
24. Garretson JA & Burton S (2003) Effects of nutrition facts panel values, nutrition claims, and health claims on consumer attitudes, perceptions of disease-related risks, and trust. J Public Policy Mark 19, 213227.
25. Kozup J, Creyer E & Burton S (2003) Making healthful food choices: the influence of health claims and nutrition information on consumers’ evaluations of packaged food products and restaurant menu items. J Mark 67, 1934.
26. Mhurchu C & Gorton D (2007) Nutrition labels and claims in New Zealand and Australia: a review of use and understanding. Aust N Z J Public Health 31, 105112.
27. Luce DR & Tukey JW (1964) Simultaneous conjoint measurement: a new type of fundamental measurement. J Math Psychol 1, 127.
28. Green PE, Krieger AM & Wind Y (2001) Thirty years of conjoint analysis: reflections and prospects. Interfaces 31, S56S73.
29. Wittink DR & Cattin P (1989) Commercial use of conjoint analysis: an update. J Mark 53, 9196.
30. Wittink DR, Vriens M & Burhenne W (1994) Commercial use of conjoint analysis in Europe: results and critical reflections. Int J Res Mark 11, 4152.
31. Harrison RW & McLennon E (2004) Analysis of consumer preferences for biotech labelling formats. J Agric Appl Econ 36, 159171.
32. Hu W, Veeman MM & Adamowicz WL (2005) Labelling genetically modified food: heterogeneous consumer preferences and the value of information. Can J Agric Econ 53, 83102.
33. Pardoe I & Simonton DK (2008) Applying discrete choice models to predict academy award winners. J R Stat Soc Ser D, The Statistician 171, 375394.
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