JOURNAL ARTICLE

Exposure to Food Temptations Reduces Subsequent Consumption Through Goal Activation.

  • Published In: International Journal of Consumer Studies, 2025, v. 49, n. 1. P. 1 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Grubliauskiene, Aiste; Liu, Yunxin; Dewitte, Siegfried 3 of 3

Abstract

The enduring availability of high‐caloric, tempting food in consumer environments has been identified as a major cause driving the obesity epidemic. The severity of the problem tends to hide the important fact that many consumers often resist food temptations. This article aims at designing consumption reduction strategies that build on the spontaneous capacity of consumers to resist food temptations. Across a series of three experiments, of which two laboratory studies and one field study, we find that physical exposure to food temptations reduces subsequent free consumption of similar foods. Building on cognitive control theory, we extend this finding and identify boundary conditions. We show that the reduction of consumption works in challenging populations (e.g., men and children) with pictures of food temptations and that it survives a delay. We also show that the effect is suppressed with explicit prohibition during pre‐exposure and with combined exposure (i.e., the combination of physical and picture temptations) in children. The findings are discussed concerning their potential as a social marketing tool. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Consumer Studies. 2025/01, Vol. 49, Issue 1, p1
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2025
  • ISSN:1470-6423
  • DOI:10.1111/ijcs.70020
  • Accession Number:183985478
  • Copyright Statement:Copyright of International Journal of Consumer Studies is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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