Assessing the influence of Nutri-Score on consumer choices in Italy: Insight from a menu planning task

Nutri-Score is a summary indicator type front-of-pack label that employs a five-color gradient (deep green to dark orange) and letters (A to E) to boost accessibility and comprehension, aiding consumers in comparing foods within the same food group. Most of the studies primarily focused on retail settings or online settings irrespective of food categories. Unlike prior studies primarily focused on retail settings, this research explores its effects on home menu planning in Italy, addressing a significant gap in the literature. This thesis investigates the potential impact of Nutri-Score on consumer decision-making in the context of dinner menu planning in Italy. Unlike prior studies primarily focused on retail settings, this research explores its effects on home menu planning in Italy, addressing a significant gap in the literature. The primary objective is to assess whether the presence of Nutri-Scores influences consumer choices in food categories and prompts shifts in different categories while planning dinner. The study identified four consumer types based on Nutri-Score choices: Category 1 (those consistently choosing the best Nutri-Score A), Category 2 (those improving Nutri-Scores across categories), and Category 3 (those improving Nutri-Scores within the same category) and Category reference (Those who do not improve). Participants favored Nutri-Score A in both dinner sources (Carbohydrate and, Protein, and fat), emphasizing the label efficacy. Unexpectedly, 26.73% of protein and fat improved nutrient scores by changing categories that were not in line with the aim of nutrient score implementation. Sociodemographic factors influenced choices, with education correlating with Nutri-Score A preference. In food categories, shifts were observed, notably in cheese preferences, challenging the efficacy of the Nutri-Score. The distribution of these categories revealed that 71.78% belonged to Category 1, 2.97% belonged to Category 2, and 25.25% belonged to Category 3. The influencing factors varied across the categories. For Category 1, higher education levels (β = 0.554, p = 0.026), valuing familiar foods, and animal welfare positively influenced choices. Category 2 showed a connection with ethical concerns (β = 1.842, p = 0.099) and higher education levels. Category 3 was associated with mood (β = -0.680, p = 0.041) and reliance on labels for food choices (β = 0.911, p = 0.083). The diverse factors that influence consumer responses to Nutri-Scores have been highlighted, emphasizing the intricate nature of dietary decision-making.

Nutri-Score is a summary indicator type front-of-pack label that employs a five-color gradient (deep green to dark orange) and letters (A to E) to boost accessibility and comprehension, aiding consumers in comparing foods within the same food group. Most of the studies primarily focused on retail settings or online settings irrespective of food categories. Unlike prior studies primarily focused on retail settings, this research explores its effects on home menu planning in Italy, addressing a significant gap in the literature. This thesis investigates the potential impact of Nutri-Score on consumer decision-making in the context of dinner menu planning in Italy. The primary objective is to assess whether the presence of Nutri-Scores influences consumer choices in food categories and prompts shifts in different categories while planning dinner. The study identified four consumer types based on Nutri-Score choices: Category 1 (those consistently choosing the best Nutri-Score A), Category 2 (those improving Nutri-Scores across categories), and Category 3 (those improving Nutri-Scores within the same category) and Category reference (Those who do not improve). Participants favored Nutri-Score A in both dinner sources (Carbohydrate and, Protein, and fat), emphasizing the label efficacy. Unexpectedly, 26.73% of protein and fat improved nutrient scores by changing categories that were not in line with the aim of nutrient score implementation. Sociodemographic factors influenced choices, with education correlating with Nutri-Score A preference. In food categories, shifts were observed, notably in cheese preferences, challenging the efficacy of the Nutri-Score. The distribution of these categories revealed that 71.78% belonged to Category 1, 2.97% belonged to Category 2, and 25.25% belonged to Category 3. The influencing factors varied across the categories. For Category 1, higher education levels (β = 0.554, p = 0.026), valuing familiar foods, and animal welfare positively influenced choices. Category 2 showed a connection with ethical concerns (β = 1.842, p = 0.099) and higher education levels. Category 3 was associated with mood (β = -0.680, p = 0.041) and reliance on labels for food choices (β = 0.911, p = 0.083). The diverse factors that influence consumer responses to Nutri-Scores have been highlighted, emphasizing the intricate nature of dietary decision-making.