Association for Public Policy Analysis & Management

This project addresses important issues in regulatory science regarding the way front-of-package (FOP) label information can encourage low-income households to make healthful food purchasing decisions. In the wake of research demonstrating that consumers have a difficult time interpreting and using government required Nutrition Facts Panels (Institute of Medicine, 2003; Carbone et al., 2012), policy makers, researchers, and those in the food industry have shifted their attention to FOP labeling (Andrews et al., 2014). To date, however, there is little consensus on what constitutes a FOP labeling system that is easily understood, not misleading, and contributes to decision making in line with consumer’s health preferences.

The study will use the iSHOPPE™ virtual store environment – a web-based three-dimensional consumer research tool that provides the benefits of having a realistic interface, similar to a real shopping experience, while allowing researchers to manipulate and control the study environment. In the iSHOPPE™ environment, consumers can wander aisles, pick up products, compare labels, and make food purchasing decisions. We will recruit 1,320 parents from low income households with children 4 to 12 years of age. Parents will be randomly assigned to one of three FOP labeling conditions (evaluative, nutrient-specific, or IOM-style hybrid labels) or assigned to a no FOP label comparison condition; they will enter the VS environment to complete a six-item shopping task. For each item (e.g., breakfast cereal, packaged lunch meat), parents will be presented with three options (very healthful, moderately healthful, low healthful). Product healthfulness will be based on the product’s nutrition facts panel and quantified using the Nutrient Profiling Model (NPM; Scarborough et al, 2007).

Parents’ comprehension of product healthfulness will be measured by asking parents to rate the perceived healthfulness of items purchased and then calculating the difference between perceived healthfulness and objective healthfulness as determined by the product’s NPM system score. The difference represents the “error” in healthfulness rating, with positive error scores indicating underestimation of product healthfulness and negative error scores indicating overestimation of product healthfulness. For the purposes of analysis, we will transform scores so that higher scores are associated with greater accuracy and lower scores are associated with over- and underestimation. Statistical analysis will involve linear regression models that estimate the mean differences in understanding of product healthfulness. Additional models will include condition-by-moderator interaction terms to examine the effects of consumer characteristics such as motivation and health knowledge.

Results of this study will provide information on the way low-income consumers interpret and use FOP nutrition information when making food purchasing decision. Additionally, our findings will be among the first to report head-to-head comparisons of the utility of different FOP labeling approaches.  By comparing labels that offer information with varying levels of complexity and completeness, we will better understand the types of information that are likely to assist consumers interested in improving the quality of their diet.