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- Creators: Department of Finance
Description
To many people, shopping is just a chore that needs to be done in order to get groceries, household supplies and clothing. To others, shopping is a sacred time where people relieve themselves of all stress and worries by spending money. Going into the mall with a lot on ones mind can be dangerous because as soon as one become distracted with items in the stores, they can truly get whisked away in spending money. Spending money and buying luxury goods actually brings a lot of excitement to people's lives. Retail therapy is defined as method to positively improve one's mood through shopping (Yarrow 2013). About 62% of consumers have purchased something to cheer themselves up and another 28% had purchased as a form of celebration (Atalay 2011). More than half of Americans participate in some sort of retail therapy in their day-to-day lives (Yarrow 2013). Retail therapy is so prevalent in people's lives because of its basis that people are striving for self-regulation of their happiness or looking for a quick and easy happiness booster. If people are motivated to want to look good or feel better about them, they are self-regulating to where they strive for these goals, which leads them to partake in retail therapy. Shopping is a chance of finding visualization for their future lives. When people go shopping and see a certain item, they automatically picture themselves in the item and what event they will be wearing the item to. It is so easy to get mentally lost during a shopping spree due to these expectations and visualizations that it feeds into people's heads. The happiness boost, the high expectations and the escape from reality are the main factors as to why shopping does indeed make people happier. This thesis will observe the concept of Retail therapy and whether or not it actually makes people happier
ContributorsDawoud, Mariam (Contributor) / Higbee, Yasmine (Contributor) / Kashiwagi, Dean (Thesis director) / Kashiwagi, Jacob (Committee member) / Department of Finance (Contributor) / Hugh Downs School of Human Communication (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Socks + Boxers began as a simple idea on a trip through Victoria's Secret as a solution to the lack of choice in quality and affordable undergarments for men. Currently, there is no central hub for men's socks and underwear. Customers shopping for men's undergarments have few choices currently: purchase an inexpensive, average quality, predetermined pack of Hanes, Fruit of the Loom, or other common undergarment brand from a store like Walmart or Target; shop for individual pieces of expensive designer underwear at a high-end department store such as Nordstrom; or, finally, purchase slightly above average quality, but fairly expensive, brand name undergarments at physical stores such as American Eagle, Urban Outfitters, or Abercrombie & Fitch, or online stores such as MeUndies. Socks + Boxers seeks to combine the accessibility and reliability of common undergarment brands, the quality and luxury-feel of high-end undergarments, and the concept of choice provided by stores that sell men's undergarment lines into a single business. We also plan to tap into the booming subscription services industry and create a way for customers to easily update and replenish their undergarment wardrobe on a regular basis with exactly what they want. In order to start out on the right foot and begin developing this business plan from the ground up, we began researching and developing a Business Model Canvas, a tool that breaks out necessary pieces of a successful business plan into easy to understand blocks. We took a critical look at the problem at hand, its potential solutions, the value the solutions provide, how we plan to start, grow, and nourish our customer base, and much more. The different pieces of this business model puzzle all come together in the following pages.
ContributorsBernat, Johnathon (Co-author) / Braaten, Joshua (Co-author) / Kashiwagi, Dean (Thesis director) / Kashiwagi, Jacob (Committee member) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / School of Accountancy (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Over the past decade, advancements in neural networks have been instrumental in achieving remarkable breakthroughs in the field of computer vision. One of the applications is in creating assistive technology to improve the lives of visually impaired people by making the world around them more accessible. A lot of research in convolutional neural networks has led to human-level performance in different vision tasks including image classification, object detection, instance segmentation, semantic segmentation, panoptic segmentation and scene text recognition. All the before mentioned tasks, individually or in combination, have been used to create assistive technologies to improve accessibility for the blind.
This dissertation outlines various applications to improve accessibility and independence for visually impaired people during shopping by helping them identify products in retail stores. The dissertation includes the following contributions; (i) A dataset containing images of breakfast-cereal products and a classifier using a deep neural (ResNet) network; (ii) A dataset for training a text detection and scene-text recognition model; (iii) A model for text detection and scene-text recognition to identify product images using a user-controlled camera; (iv) A dataset of twenty thousand products with product information and related images that can be used to train and test a system designed to identify products.
This dissertation outlines various applications to improve accessibility and independence for visually impaired people during shopping by helping them identify products in retail stores. The dissertation includes the following contributions; (i) A dataset containing images of breakfast-cereal products and a classifier using a deep neural (ResNet) network; (ii) A dataset for training a text detection and scene-text recognition model; (iii) A model for text detection and scene-text recognition to identify product images using a user-controlled camera; (iv) A dataset of twenty thousand products with product information and related images that can be used to train and test a system designed to identify products.
ContributorsPatel, Akshar (Author) / Panchanathan, Sethuraman (Thesis advisor) / Venkateswara, Hemanth (Thesis advisor) / McDaniel, Troy (Committee member) / Arizona State University (Publisher)
Created2020