Influence of nutritional warnings and other label features on consumers' choice: Results from an eye-tracking study

眼动 广告 食品科学 计算机科学
作者
Giuliana Tórtora,Leandro Machín,Gastón Ares
出处
期刊:Food Research International [Elsevier]
卷期号:119: 605-611 被引量:39
标识
DOI:10.1016/j.foodres.2018.10.038
摘要

Nutritional warnings highlighting products with high content of nutrients associated with non-communicable diseases have emerged as a potentially efficient alternative to discourage consumption of unhealthful products. However, the relative influence of this front-of-pack nutrition information scheme compared to other label features is still under researched. In this context, the aim of the present work was to study the influence of nutritional warnings and other information commonly displayed on food labels to convey health associations on consumers' choice and visual attention. A choice-conjoint study using labels of cookies and crackers was implemented considering the following three variables: front-of-pack nutrition information (nutritional warnings vs. facts up front panel), nutrient claim (present vs. absent) and type of product (conveying health vs. hedonic associations). A total of 124 people completed the task using the monitor of an eye-tracker. Participants were asked to select the label they would prefer to buy if they were in a supermarket. Nutritional warnings were efficient in attracting consumers' attention and required less time and fewer fixations to process than the facts up front panel. In addition, their inclusion on the labels significantly discouraged consumers' choice of labels. However, type of product was the most important variable influencing the choice of both cookies and crackers. Results from the present work confirm the potential of nutritional warnings as directive front-of-pack nutrition labelling scheme and stress that they cannot be expected to cause radical changes in consumers' eating habits due to their competition with other product features. Instead, they are expected to encourage consumers to select the most healthful option within a product category.

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