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This paper will be exploring a marketing plan for a Kpop Fan artist, Jennifer Lee. Kpop is a genre of music originating from South Korea that provides a whole-package entertainment. Fan artists are producers who create produce for the consumption and purchase of other Kpop fans. The paper will consider

This paper will be exploring a marketing plan for a Kpop Fan artist, Jennifer Lee. Kpop is a genre of music originating from South Korea that provides a whole-package entertainment. Fan artists are producers who create produce for the consumption and purchase of other Kpop fans. The paper will consider segmentation and the products and platforms that best target them in order to maximize revenue. A survey was performed with a sample size of 314 participants to find out consumer behavior and preference as well as producer situation. Consumers come from both the United States and abroad. Customers come directly and almost exclusively from followers. Therefore, increasing the number of followers on Instagram is essential to increasing revenue. Jennifer has time, resource, and ability constraints, while the market has limited potential. The conclusion is that Jennifer should become more organized as a business. To grow her following, she should cater more towards the most popular fandoms (BTS), make art tutorials, consider collaborations, and better inform followers of her products/services available for purchase. The social media platforms key to marketing Jennifer's products are Instagram and Twitter. Other platforms to be used to increase exposure are Tumblr, Amino Apps, DeviantArt, Reddit, and YouTube. She must also declutter all of these virtual storefronts of unnecessary content to varying degrees in order to build ease of access and a trustworthy brand image. The best platforms for transaction is a personal store, RedBubble (a website that allows users to sell a variety of products with their uploaded images printed onto them), Patreon, and in-person at conventions.
ContributorsXu, Everest Christine (Author) / Eaton, Kathryn (Thesis director) / Ingram-Waters, Mary (Committee member) / Department of Marketing (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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Description
The advertising agency, in its variety of forms, is one of the most powerful forces in the modern world. Its products are seen globally through various multimedia outlets and they strongly impact culture and economy. Since its conception in 1843 by Volney Palmer, the advertising agency has evolved into the

The advertising agency, in its variety of forms, is one of the most powerful forces in the modern world. Its products are seen globally through various multimedia outlets and they strongly impact culture and economy. Since its conception in 1843 by Volney Palmer, the advertising agency has evolved into the recognizable—and unrecognizable—firms scattered around the world today. In the United States alone, there are roughly 13.4 thousand agencies, many of which also have branches in other countries. The evolution of the modern advertising agency coincided with, and even preceded, some of the major inflection points in history. Understanding how and why changes in advertising agencies affected these inflection points provides a glimpse of understanding into the relationship between advertising, business, and societal values.

In the pages ahead we will explore the future of the advertising industry. We will analyze our research to uncover the underlying trends pointing towards what is to come and work to apply those explanations to our understanding of advertising in the future.
ContributorsHarris, Chase (Co-author) / Potthoff, Zachary (Co-author) / Gray, Nancy (Thesis director) / Samper, Adriana (Committee member) / Department of Information Systems (Contributor) / Department of Marketing (Contributor) / Herberger Institute for Design and the Arts (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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Description
Object localization is used to determine the location of a device, an important aspect of applications ranging from autonomous driving to augmented reality. Commonly-used localization techniques include global positioning systems (GPS), simultaneous localization and mapping (SLAM), and positional tracking, but all of these methodologies have drawbacks, especially in high traffic

Object localization is used to determine the location of a device, an important aspect of applications ranging from autonomous driving to augmented reality. Commonly-used localization techniques include global positioning systems (GPS), simultaneous localization and mapping (SLAM), and positional tracking, but all of these methodologies have drawbacks, especially in high traffic indoor or urban environments. Using recent improvements in the field of machine learning, this project proposes a new method of localization using networks with several wireless transceivers and implemented without heavy computational loads or high costs. This project aims to build a proof-of-concept prototype and demonstrate that the proposed technique is feasible and accurate.

Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.

As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.
ContributorsChang, Roger (Co-author) / Kann, Trevor (Co-author) / Alkhateeb, Ahmed (Thesis director) / Bliss, Daniel (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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Description
At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson’s disease classification and severity assessment.

At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson’s disease classification and severity assessment. An automated, stable, and accurate method to evaluate Parkinson’s would be significant in streamlining diagnoses of patients and providing families more time for corrective measures. We propose a methodology which incorporates TDA into analyzing Parkinson’s disease postural shifts data through the representation of persistence images. Studying the topology of a system has proven to be invariant to small changes in data and has been shown to perform well in discrimination tasks. The contributions of the paper are twofold. We propose a method to 1) classify healthy patients from those afflicted by disease and 2) diagnose the severity of disease. We explore the use of the proposed method in an application involving a Parkinson’s disease dataset comprised of healthy-elderly, healthy-young and Parkinson’s disease patients.
ContributorsRahman, Farhan Nadir (Co-author) / Nawar, Afra (Co-author) / Turaga, Pavan (Thesis director) / Krishnamurthi, Narayanan (Committee member) / Electrical Engineering Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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Description
In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form

In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form a dependency tree. An agent operating within these environments have access to low amounts of data about the environment before interacting with it, so it is crucial that this agent is able to effectively utilize a tree of dependencies and its environmental surroundings to make judgements about which sub-goals are most efficient to pursue at any point in time. A successful agent aims to minimizes cost when completing a given goal. A deep neural network in combination with Q-learning techniques was employed to act as the agent in this environment. This agent consistently performed better than agents using alternate models (models that used dependency tree heuristics or human-like approaches to make sub-goal oriented choices), with an average performance advantage of 33.86% (with a standard deviation of 14.69%) over the best alternate agent. This shows that machine learning techniques can be consistently employed to make goal-oriented choices within an environment with recursive sub-goal dependencies and low amounts of pre-known information.
ContributorsKoleber, Derek (Author) / Acuna, Ruben (Thesis director) / Bansal, Ajay (Committee member) / W.P. Carey School of Business (Contributor) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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Description
This paper looks at the growth of influencer marketing in application and how it has shifted the relationship between brands and consumers. Barriers to enter the space and methods of practice are discussed and analyzed to project the accessibility of obtaining influencer status. Best practices for brands and influencers are

This paper looks at the growth of influencer marketing in application and how it has shifted the relationship between brands and consumers. Barriers to enter the space and methods of practice are discussed and analyzed to project the accessibility of obtaining influencer status. Best practices for brands and influencers are outlined based on research, and key findings are analyzed from interviewed participants that play an active role in the field. Another component of the paper includes the discussion of the significance of platform dependence regarding influencers and brands using social media channels to reach consumers. The dynamic of the relationship that exists between consumers, brands and platforms is demonstrated through a model to demonstrate the interdependence of the relationship. The final component of the paper involves the exploration of the field as an active participant through an experiment that was conducted by the researcher on behalf of the question: can anyone be an influencer? The answer to this question is explored through personal accounts on the journey during an eight month process of testing content creation and promotion to build awareness and increase engagement. The barriers to enter the space as an influencer and to collaborate with brands is addressed through the process of testing tactics and strategies on social channels, along with travel expeditions across Arizona to contribute to content creation purposed into blog articles. The findings throughout the paper are conclusive that the value of influencer marketing is increasing as more brands validate and utilize this method in their marketing efforts.
ContributorsDavis, Natalie Marie (Author) / Giles, Bret (Thesis director) / Schlacter, John (Committee member) / Department of Information Systems (Contributor) / Department of Marketing (Contributor) / Walter Cronkite School of Journalism and Mass Communication (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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Description
This thesis dives into the world of artificial intelligence by exploring the functionality of a single layer artificial neural network through a simple housing price classification example while simultaneously considering its impact from a data management perspective on both the software and hardware level. To begin this study, the universally

This thesis dives into the world of artificial intelligence by exploring the functionality of a single layer artificial neural network through a simple housing price classification example while simultaneously considering its impact from a data management perspective on both the software and hardware level. To begin this study, the universally accepted model of an artificial neuron is broken down into its key components and then analyzed for functionality by relating back to its biological counterpart. The role of a neuron is then described in the context of a neural network, with equal emphasis placed on how it individually undergoes training and then for an entire network. Using the technique of supervised learning, the neural network is trained with three main factors for housing price classification, including its total number of rooms, bathrooms, and square footage. Once trained with most of the generated data set, it is tested for accuracy by introducing the remainder of the data-set and observing how closely its computed output for each set of inputs compares to the target value. From a programming perspective, the artificial neuron is implemented in C so that it would be more closely tied to the operating system and therefore make the collected profiler data more precise during the program's execution. The program is designed to break down each stage of the neuron's training process into distinct functions. In addition to utilizing more functional code, the struct data type is used as the underlying data structure for this project to not only represent the neuron but for implementing the neuron's training and test data. Once fully trained, the neuron's test results are then graphed to visually depict how well the neuron learned from its sample training set. Finally, the profiler data is analyzed to describe how the program operated from a data management perspective on the software and hardware level.
ContributorsRichards, Nicholas Giovanni (Author) / Miller, Phillip (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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Description
Health and fast food are seemingly on two opposite ends of the spectrum, yet healthy fast food is quickly growing in popularity. As many fast food brands are adjusting their menu to accommodate to this trend, this study explores how health claims used in fast food advertising affect college students'

Health and fast food are seemingly on two opposite ends of the spectrum, yet healthy fast food is quickly growing in popularity. As many fast food brands are adjusting their menu to accommodate to this trend, this study explores how health claims used in fast food advertising affect college students' perceptions of health and their likelihood to purchase healthy fast food products. To test this, a survey gathered quantitative data to assess student's perceptions of health and fast food, as well as qualitative data of when eating healthy is appealing and unappealing. An ad manipulation was employed to test student's likelihood to purchase the product shown in the ad. Though the study did not yield significant results, the results collected indicate that health claims may not be enough to influence someone to purchase, but that taste is of student's highest priority when making food purchase decisions. Thus, the study opens the door for future research in this realm of health and fast food, and concludes with recommendations for both marketers and future researchers.
ContributorsMigray, Emilee Catherine (Author) / Gray, Nancy (Thesis director) / Samper, Adriana (Committee member) / Department of Marketing (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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Description
The National Basketball Association (NBA) is one of the Big Four Sporting Leagues of US Professional Sports. In recent years, the NBA has enjoyed milestone seasons in both attendance and television ratings, resulting in steady increases to both, over the previous decade. (Morgan, 2017) This surge can be attributed in

The National Basketball Association (NBA) is one of the Big Four Sporting Leagues of US Professional Sports. In recent years, the NBA has enjoyed milestone seasons in both attendance and television ratings, resulting in steady increases to both, over the previous decade. (Morgan, 2017) This surge can be attributed in part to the integration of "cultural recognition" initiatives and the overall message of inclusivity on the part of NBA franchises, with their respective promotions and advertisements such as television, social media, radio, etc. Heritage Nights, such as "Noche Latina," among other variants in the NBA, typically feature culturally influenced changes to team logos, giveaways, and other consumer offerings. In markets where Hispanics make up a significant percentage of the fan-base, such as Phoenix, NBA franchises such as the Phoenix Suns must ascertain the financial or perceptual impacts, associated with risks of stereotyping, offending or otherwise unintentionally alienating different categories of fans. To this end, data was collected from the local NBA franchises' fanbase, specifically Phoenix Suns season-ticket holders, and was statistically checked for significant relationships between both categories of fans and several different variables. This analysis found that only $192K in revenue is being missed through the investment of Heritage Nights, and that fan perceptions of stereotypical or offensive giveaways and practices have no significant effect on game or event attendance, despite the stereotypes toward giveaways and practices still being present. Implications of this study provide possible next steps for the Suns and continue to widen the scope of demographical sports marketing both in professional basketball and beyond.
ContributorsGibbens, Patrick Alexander (Author) / Eaton, John (Thesis director) / McIntosh, Daniel (Committee member) / Department of Supply Chain Management (Contributor) / School of Music (Contributor) / Department of Marketing (Contributor) / W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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Description
In this study, the packaging and labeling of milk and coffee was compared between Walmart and Sprouts. The pricing, the sourcing, the certifications and the overall shelf presence of the items was taken under consideration. After studying the packaging of both, a new design incorporating the applicable labels, customer appeal

In this study, the packaging and labeling of milk and coffee was compared between Walmart and Sprouts. The pricing, the sourcing, the certifications and the overall shelf presence of the items was taken under consideration. After studying the packaging of both, a new design incorporating the applicable labels, customer appeal and appropriate green marketing was created for both the commodities.
ContributorsBhatt, Rashi Hitesh (Author) / Collins, Shari (Thesis director) / Keahey, Jennifer (Committee member) / School of International Letters and Cultures (Contributor) / School of Earth and Space Exploration (Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05