Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Food Science and Human Nutrition

Committee Member

Dr. Rupert Andrew Hurley, Committee Chair

Committee Member

Dr. Pradip Srimani

Committee Member

Dr. Paul Dawson

Committee Member

Dr. Elliot Jesch


Within the past three years, approximately two hundred one-off studies have occurred in developing eye tracking methodologies for quantifying consumer attention on packaging design preference. Due to restricted time and resources, most research analyzes consumer attention regarding a relatively small amount of products within specific planogram. However, larger questions concerning category trends and insights are not possible with these smaller, one-off studies. Broad data aggregation and analysis is important for understanding packaging design per product category in order to understand category-wide design trends and insights. Considering the excessive effort in manual data retrieval, analysis, and reporting, a method that can improve efficiency and data aggregation would be of tremendous benefit to all researchers studying consumer behavior on relevant product categories. Ultimately, the application of the relational database management system fits this need. The work herein describes a procedure of developing a database-driven management system for retail food packaging eye tracking studies and the data analytics. A relational database of eye tracking studies associated with a web portal was designed and created to aggregate, store, access, share, and analyze eye tracking data based on studies in an immersive retail environment. The comparison between this system and the file-based methods was discussed, includes eye tracking study procedure, data usage efficiency, and data analysis methodology. This body of work covers 14 eye tracking projects in aggregate, involving 34 planograms comprised of a total of 421 consumer product goods. When using this database-driven system compared to the traditional file-based process in terms of time, it was found that the developed system reduced the time of the eye tracking study process by 48%.



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