When Food Scanner Apps Outperform Front-of-Pack Nutrition Labels: A Conditional Process Model to Foster Healthier Food Choices in Times of Growing Distrust

Abstract

While consumers increasingly use food scanner apps as an alternative to front-of-pack labels, the effectiveness of these nutritional signals in promoting healthier food choices remains a topic of debate. This study examines when and under what conditions food scanner apps encourage healthier eating and outperform front-of-pack nutrition labels. It builds on four studies: a quantitative content analysis of over 16,000 online reviews and a survey of 86 respondents in an exploratory phase, along with two experiments that test the conceptual model. The findings identify three conditions where food scanner apps outperform front-of-pack labels: when the nutritional quality score of a product is poor, when consumers distrust the dominant players, and when mid-range brands have average brand equity. These findings suggest that food scanner apps and front-of-pack labels complement rather than oppose each other, thus providing key insights for policymakers and food brand managers.