Two-handed Design: Development of Food Personality Framework Using Mixed Method Needfinding

In this case study, we present a mixed methodology approach to needfinding, integrating in-depth qualitative interview data with machine learning-powered analysis of a larger dataset. The research is motivated by the high failure rates and low involvement of consumers in the food startup industry’s product design process. To help food startups design products in a more consumer-friendly and timely manner, we are developing a novel framework called the Food Personality Framework (FPF). This framework categorizes eaters according to their eating habits, preferences, motivations and constraints. To better understand the complex relationships between motivations, we chose Grounded Theory as the most pertinent approach and interviewed 14 singles with full autonomy over their food choices. We further leveraged the availability of large online food-related datasets to inform and reinforce our findings from the qualitative work. We analyzed 6687 user behaviors of Food.com, a popular recipe recommendation site, according to the 18 influencers of food choice identified from the qualitative interviews. We found three meaningful clusters of user behavior: Minimalists, Social Butterflies and Conscious eaters. The interview data, thus, enabled grounded classification of the large scale user behavior and provided a grounded way to interpret the relationship among the top motivators identified in each clusters. The cluster analysis will inform future sampling of interviewees and will provide new insightful questions for the qualitative research. The case study delineates a dynamic interplay of qualitative and quantitative data used to investigate human food choice, a novel domain in the Human-Food Interaction literature.