Nutri-scores: The impact on healthy purchasing behavior

Colin Broer MSc Marketing Intelligence MSc Marketing Management Thesis defense 24-01-2021

Nutri-scores:

Table of Contents

5.

Conclusion & Discussion

4.

Results

3.

Methodology

2.

Theoretical framework

Introduction

❏ More and more people are coping with an increase in overweight and obesity, followed by chronic diseases (i.e. heart attacks)

❏ One of the main causes is the change in eating behaviors, consumers have increased excessive energy intake (Smethers & Rolls, 2018)

❏ Modern supermarkets have developed to the point that consumers are overloaded with

information and choice → Consumers fall back on heuristics (Ketron, Spear & Dai, 2016; Garcia & Barreiro-Hurlé, 2019)

❏ The Nutri-score could help consumers make conscious choices

Theoretical Framework

❏ Conflicting literature, benefits and cons

Information overload

❏ Similar to choice overload

❏ Consumers have limited cognitive capabilities (Eppler & Mengis, 2008)

Theoretical Framework

❏ Colour graded independent summary of nutritional values

❏ Reduces the amount of information on packages (Hagmann, & Siegrist, 2020)

Interactive decision aids

Two distinct models used

Multiple Linear Regression (MLR) model

❏ Satisfied statistical assumptions (Myers, 1990); Linearity, Multicollinearity, Homoscedasticity & Normality

Truncated negative binomial model

Results

❏ Nutri-scores do not significantly improve the healthiness of consumer groceries

❏ No learning effect was present, but phase 2 was significantly healthier than phase 1 (β= –.049, p < .001)

❏ Both filtering and sorting based on the Nutri-score improved the healthiness of consumer groceries (β= -.069, p < .001) and (β= -.0414, p < .001) respectively

Results

❏ Intercept (β= .528, p < .001)

❏ Implementing the Nutri-score does not positively or negatively influence the basket size ❏ The unhealthier the product, the more it gets sold (β= .029, p < .001)*

❏ Sorting based on price (dummy) decreases the basket size (β= .029, p < .001)* ❏ Composed recipe list negatively influence the basket size (β= -.025, p < .001)*

Conclusion

❏ If properly implemented, the Nutri-score can be beneficial

❏ Decision aids help consumers make healthy choices ❏ No significant learning effect was found in the

Limitations

❏ The MLR model excluded observations with less that 5 products added ❏ The violation of homoscedasticity is assumed to be resolved