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Different Shades of Nudges: Moderating Effects of Individual Characteristics and States on the Effectiveness of Nudges during a Fast-Food Order

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  • Irina Dolgopolova

    (Department of Marketing and Supply Chain Management, School of Business and Economics, Maastricht University, 6211 LK Maastricht, The Netherlands)

  • Alessia Toscano

    (Marketing and Consumer Research, School of Management, Technical University of Munich, 85354 Freising, Germany)

  • Jutta Roosen

    (Marketing and Consumer Research, School of Management, Technical University of Munich, 85354 Freising, Germany)

Abstract

Nudges, or subtle changes to a choice environment, are increasingly used in online food ordering platforms to improve dietary choices and reduce calorie intake. We report the results of an experiment aimed at nudging young adults to reduce calories in a fast-food order (N = 994). The nudging interventions used were: an order assistant, a color-coded system, and a combination of the order assistant and color-coded system. We hypothesized that participants’ characteristics (sex, BMI, education) and states (positive affect, negative affect, hunger) moderate the effectiveness of nudges. Our analysis shows that the effect of nudges is slightly increasing at higher BMI levels. In the combined treatment, hunger and negative affect significantly moderate the effect of nudges. We do not observe the moderating effects of participants’ sex, educational level, and positive affect in any of the treatments.

Suggested Citation

  • Irina Dolgopolova & Alessia Toscano & Jutta Roosen, 2021. "Different Shades of Nudges: Moderating Effects of Individual Characteristics and States on the Effectiveness of Nudges during a Fast-Food Order," Sustainability, MDPI, vol. 13(23), pages 1-12, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:23:p:13347-:d:693406
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    References listed on IDEAS

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    1. Bonanno, Alessandro & Bimbo, Francesco & Cleary, Rebecca & Castellari, Elena, 2018. "Food labels and adult BMI in Italy – An unconditional quantile regression approach," Food Policy, Elsevier, vol. 74(C), pages 199-211.
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    3. Romain Cadario & Pierre Chandon, 2020. "Which Healthy Eating Nudges Work Best? A Meta-Analysis of Field Experiments," Marketing Science, INFORMS, vol. 39(3), pages 465-486, May.
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    Cited by:

    1. Phillip Oliver Gottschewski-Meyer & Marvin Auf der Landwehr & Nicole Lüddemann & Christoph Viebahn, 2024. "Trade-offs and synergies of digital choice environments: Towards a taxonomy and configurational model," Electronic Markets, Springer;IIM University of St. Gallen, vol. 34(1), pages 1-21, December.
    2. repec:ags:aaea22:335733 is not listed on IDEAS
    3. Giulia Andreani & Rungsaran Wongprawmas & Beatrice Biasini & Alice Rosi & Cinzia Franchini & Irina Dolgopolova & Cristina Mora & Davide Menozzi & Francesca Scazzina & Miguel I. Gómez & Jutta Roosen & , 2024. "Digital nudging at the university canteen: an online study with American young adults," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 12(1), pages 1-22, December.
    4. S. Mills & S. Costa & C. R. Sunstein, 2023. "AI, Behavioural Science, and Consumer Welfare," Journal of Consumer Policy, Springer, vol. 46(3), pages 387-400, September.
    5. Söderlund, Magnus, 2023. "Moderator variables in consumer research: A call for caution," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).

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