Objective: This study measured observers’ abilities to identify letter targets distributed among number distractors in a virtual shopping environment. Head-turning behavior was also continuously recorded throughout each trial. The data were then used to test whether a model’s prediction for the duration of visibility needed for target detection in a virtual shopping environment (Parikh & Mowrey, 2014) generalize to the more realistic shopping task of identifying a target on a shelf. Currently, the model predicts the visibility of the locations of targets in traditional racks oriented 90° to the aisle (perpendicular) as well as racks oriented at 30°, 45, 135°, and 150° to the central aisle. Background: Exposure (whether a portion of the rack is seen) and intensity (how long that rack portion is seen) are the two variables of interest in the model. According to the analytical and computational models developed by Parikh and Mowrey (2014), traditional 90° racks in retail shopping environments result in lower exposure and intensity than racks at other angles. A previous study confirmed these model predictions with a simple target detection task (small red targets on empty grey racks) in a virtual environment. However, discriminating a target on a stocked shelf requires more time and is more representative of typical shopping behavior. Methods: The 24 participants completed 10 target discrimination trials as they were moved through a virtual shopping environment. Hypothesis: We hypothesized and found a significant effect of orientation on discrimination performance. Additionally, we hypothesized that the percentage of total targets correctly identified would be lower than the simple detection rate in Parikh and Mowrey (2014) but found mixed results. Model fit was first assessed via a d’ metric. The d’ values were generally low, but they were best at intensities higher than that needed for detection due to the additional time needed to identify the targets among distractors. However, the observed non-normal distributions of hits and false alarms make the d’ analysis difficult to interpret. Subsequently, a chi-square analysis was done. The chi-square analysis also showed evidence for higher intensities needed for discrimination than for detection in the 30°, 45°, and 90° rack orientations. Limitations and modifications needed for the model to achieve a better match to human discrimination performance are discussed. Advisors/Committee Members: Watamaniuk, Scott (Advisor).

The retail industry in the U.S. contributed 1.14 trillion in value added (or 5.9%) to the GDP in 2017, an increase of 3.7% from the previous year. While store closures have dominated the news in the recent past (e.g., Toys-R-Us, Sears, and Bon-Ton) due to ineffective supply chain practices, inadequate in-store experiences, and competition from e-tailers, other retailers such as Ross, T. J. Maxx, Burlington Coat Factory, and Kroger have been expanding their footprint. Brick-and-mortar stores are unique as they allow shoppers the ability to see, touch, and try products, in addition to exploring new products. Kohl’s CEO has even indicated that 90% of their revenue is still generated in brick-and-mortar stores. Besides reducing supply chain costs, retailers have been paying considerable attention to redesigning their stores by varying layouts and displays to improve shopping experience and remain profitable. However, a lack of scientific methods that correlate layout changes to improved experience has often led to time-consuming and expensive trial-and-error approaches for the retailers.This research focuses on the design of such brick-and-mortar stores by developing a quantitative approach that models the visual interaction between a 3D shopper’s field of view and the rack layout. This visual interaction has been shown to influence shopper purchasing habits and their overall experience. While some metrics for visual experience have been proposed in the literature, they have been limited in many ways. The objective of this research is to develop new models to quantify visual experience and employ them in layout design models.Our first contribution consists of quantifying exposure (which rack locations are seen) and the intensity of exposure (how long they are seen) by accounting for the dynamic interaction between the human 3D field of regard with a 3D rack layout. We consider several rack designs/layouts that we noticed at nearby retail stores, ranging from the typical rectangular racks placed orthogonal to the main aisle to racks with varying orientations, curvatures, and heights. We model this 3D layout problem as a series of 2D problems while accounting for obstructions faced by shoppers during their travel path (both uni- and bi-directional). We also validate our approach through a human subjects study in a Virtual Environment. Our findings suggest that curving racks in a layout with racks oriented at 90° could increase exposure by 3-121% over straight racks. Further, several layout designs could increase exposure by over 500% with only a 20% increase in floor space. In our second contribution, we introduce the Rack Orientation and Curvature Problem (ROCP) for a retail store, which determines the best rack orientation and curvature that maximizes marginal impulse profit (after discounting for floor space cost). We derive impulse profit considering the probability a shopper will see a product category, the probability the shopper will purchase a product from that category if seen, and the product category’s unit… Advisors/Committee Members: Parikh, Pratik (Advisor).