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Description
Millennials are the group of people that make up the newer generation of the world's population and they are constantly surrounded by technology, as well as known for having different values than the previous generations. Marketers have to adapt to newer ways to appeal to millennials and secure their loyalty since millennials are always on the lookout for the next best thing and will "trade up for brands that matter, but trade down when brand value is weak", it poses a challenge for the marketing departments of companies (Fromm, J. & Parks, J.). The airline industry is one of the fastest growing sectors as "the total number of people flying on U.S. airlines will increase from 745.5 million in 2014 and grow to 1.15 billion in 2034," which shows that airlines have a wider population to market to, and will need to improve their marketing strategies to differentiate from competitors (Power). The financial sector also has a difficult time reaching out to millennials because "millennials are hesitant to take financial risks," as well as downing in college debt, while not making as much money as previous generations (Fromm, J. & Parks, J.). By looking into the marketing strategies, specifically using social media platforms, of the two industries, an understanding can be gathered of what millennials are attracted to. Along with looking at the marketing strategies of financial and airline industries, I looked at the perspectives of these industries in different countries, which is important to look at because then we can see if the values of millennials vary across different cultures. Countries chosen for research to further examine their cultural differences in terms of marketing practices are the United States and England. The main form of marketing that was used for this research were social media accounts of the companies, and seeing how they used the social networking platforms to reach and engage with their consumers, especially with those of the millennial generation. The companies chosen for further research for the airline industry from England were British Airways, EasyJet, and Virgin Atlantic, while for the U.S. Delta Airlines, Inc., Southwest Airlines, and United were chosen. The companies chosen to further examine within the finance industry from England include Barclay's, HSBC, and Lloyd's Bank, while for the U.S. the banks selected were Bank of America, JPMorgan Chase, and Wells Fargo. The companies for this study were chosen because they are among the top five in their industry, as well as all companies that I have had previous interactions with. It was meant to see what the companies at the top of the industry were doing that set them apart from their competitors in terms of social media marketing content and see if there were features they lacked that could be changed or improvements they could make. A survey was also conducted to get a better idea of the attitudes and behaviors of millennials when it comes to the airline and finance industries, as well as towards social media marketing practices.
ContributorsPathak, Krisha Hemanshu (Author) / Kumar, Ajith (Thesis director) / Arora, Hina (Committee member) / W. P. Carey School of Business (Contributor) / Department of Information Systems (Contributor) / Department of Marketing (Contributor) / Hugh Downs School of Human Communication (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Alternative currencies have a long and varied history, in which Bitcoin is the latest chapter. The pseudonymous Satoshi Nakamoto created Bitcoin as an implementation of the concept of a cryptocurrency, or a decentralized currency based on the principles of cryptography. Since its creation in 2008, Bitcoin has had a fairly tumultuous existence that limited its adoption. Wide price fluctuations occurred as the appeal of free money by running a piece of computer software drove people to purchase expensive hardware, and high-profile scandals cast Bitcoin as an unstable currency well-suited primarily for purchasing illicit materials. Consumer confidence in the currency was extremely low, and businesses were extremely hesitant to accept a currency that could easily lose half (or more) of its value overnight. However, recent years have seen the currency begin to stabilize as businesses and mainstream investors have begun to accept and support it. Alternative cryptocurrencies, titled "altcoins," have also been created to fill market niches that Bitcoin was not addressing. Governmental intervention, a concern of many following the currency, has been surprisingly restrained and has actually contributed to its stability. The future of Bitcoin looks very bright as it carries the dream of the alternative currency forward into the 21st century.
ContributorsReardon, Brett (Co-author) / Burke, Ryan (Co-author) / Happel, Stephen (Thesis director) / Boyes, William (Committee member) / School of Politics and Global Studies (Contributor) / Department of Information Systems (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Arizona and Florida are unique venues are they are the only two locations in the world to host the preseason leagues known as Spring Training for all thirty Major League Baseball teams. With fan bases willing to travel and spend disposable income to follow their favorite teams and/or escape the cold spells of their home state, the sports and tourism industries in Arizona and Florida have been able to captivate a status as top spring destinations. This study takes a focus on the economic impact that Spring Training in March has on the state of Arizona; specifically the Phoenix Metropolitan area. Consumer research is presented and a SWOT analysis is generated to further assess the condition of the Cactus League and Arizona as a host state. An economic impact study driven by the Strengths, Weaknesses, Opportunities & Threats (SWOT) analysis method is the primary focuses of research due to the sum and quality of usable data that can be organized using the SWOT structure. The scope of this research aims to support the argument that Spring Training impacts the host city in which it resides in. In conjunction with the SWOT analysis, third parties will be able to get a sense of the overall effectiveness and impact of Cactus League Spring Training in the Valley of the Sun. Integration of findings from a Tampa Bay sight visit will also be assessed to determine the health of the competition. This study will take an interdisciplinary approach as it views the topics at hand from the lenses of the consumer, baseball professional, and investor.
ContributorsOlden, Kyle (Co-author) / Farmer, James (Co-author) / Eaton, John (Thesis director) / Mokwa, Michael (Committee member) / T. Denny Sanford School of Social and Family Dynamics (Contributor) / College of Public Service and Community Solutions (Contributor) / Department of Information Systems (Contributor) / Department of Economics (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Introduction: There are 350 to 400 pediatric heart transplants annually according to the Pediatric Heart Transplant Database (Dipchand et al. 2014). Finding appropriate donors can be challenging especially for the pediatric population. The current standard of care is a donor-to-recipient weight ratio. This ratio is not necessarily a parameter directly indicative of the size of a heart, potentially leading to ill-fitting allografts (Tang et al. 2010). In this paper, a regression model is presented - developed by correlating total cardiac volume to non-invasive imaging parameters and patient characteristics – for use in determining ideal allograft fit with respect to total cardiac volume.
Methods: A virtual, 3D library of clinically-defined normal hearts was compiled from reconstructed CT and MR scans. Non-invasive imaging parameters and patient characteristics were collected and subjected to backward elimination linear regression to define a model relating patient parameters to the total cardiac volume. This regression model was then used to retrospectively accept or reject an ‘ideal’ donor graft from the library for 3 patients that had undergone heart transplantation. Oversized and undersized grafts were also transplanted to qualitatively analyze virtual transplantation specificity.
Results: The backward elimination approach of the data for the 20 patients rejected the factors of BMI, BSA, sex and both end-systolic and end-diastolic left ventricular measurements from echocardiography. Height and weight were included in the linear regression model yielding an adjusted R-squared of 82.5%. Height and weight showed statistical significance with p-values of 0.005 and 0.02 respectively. The final equation for the linear regression model was TCV = -169.320+ 2.874h + 3.578w ± 73 (h=height, w=weight, TCV= total cardiac volume).
Discussion: With the current regression model, height and weight significantly correlate to total cardiac volume. This regression model and virtual normal heart library provide for the possibility of virtual transplant and size-matching for transplantation. The study and regression model is, however, limited due to a small sample size. Additionally, the lack of volumetric resolution from the MR datasets is a potentially limiting factor. Despite these limitations the virtual library has the potential to be a critical tool for clinical care that will continue to grow as normal hearts are added to the virtual library.
Methods: A virtual, 3D library of clinically-defined normal hearts was compiled from reconstructed CT and MR scans. Non-invasive imaging parameters and patient characteristics were collected and subjected to backward elimination linear regression to define a model relating patient parameters to the total cardiac volume. This regression model was then used to retrospectively accept or reject an ‘ideal’ donor graft from the library for 3 patients that had undergone heart transplantation. Oversized and undersized grafts were also transplanted to qualitatively analyze virtual transplantation specificity.
Results: The backward elimination approach of the data for the 20 patients rejected the factors of BMI, BSA, sex and both end-systolic and end-diastolic left ventricular measurements from echocardiography. Height and weight were included in the linear regression model yielding an adjusted R-squared of 82.5%. Height and weight showed statistical significance with p-values of 0.005 and 0.02 respectively. The final equation for the linear regression model was TCV = -169.320+ 2.874h + 3.578w ± 73 (h=height, w=weight, TCV= total cardiac volume).
Discussion: With the current regression model, height and weight significantly correlate to total cardiac volume. This regression model and virtual normal heart library provide for the possibility of virtual transplant and size-matching for transplantation. The study and regression model is, however, limited due to a small sample size. Additionally, the lack of volumetric resolution from the MR datasets is a potentially limiting factor. Despite these limitations the virtual library has the potential to be a critical tool for clinical care that will continue to grow as normal hearts are added to the virtual library.
ContributorsSajadi, Susan (Co-author) / Lindquist, Jacob (Co-author) / Frakes, David (Thesis director) / Ryan, Justin (Committee member) / Harrington Bioengineering Program (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Glioblastoma multiforme (GBM) is a malignant, aggressive and infiltrative cancer of the central nervous system with a median survival of 14.6 months with standard care. Diagnosis of GBM is made using medical imaging such as magnetic resonance imaging (MRI) or computed tomography (CT). Treatment is informed by medical images and includes chemotherapy, radiation therapy, and surgical removal if the tumor is surgically accessible. Treatment seldom results in a significant increase in longevity, partly due to the lack of precise information regarding tumor size and location. This lack of information arises from the physical limitations of MR and CT imaging coupled with the diffusive nature of glioblastoma tumors. GBM tumor cells can migrate far beyond the visible boundaries of the tumor and will result in a recurring tumor if not killed or removed. Since medical images are the only readily available information about the tumor, we aim to improve mathematical models of tumor growth to better estimate the missing information. Particularly, we investigate the effect of random variation in tumor cell behavior (anisotropy) using stochastic parameterizations of an established proliferation-diffusion model of tumor growth. To evaluate the performance of our mathematical model, we use MR images from an animal model consisting of Murine GL261 tumors implanted in immunocompetent mice, which provides consistency in tumor initiation and location, immune response, genetic variation, and treatment. Compared to non-stochastic simulations, stochastic simulations showed improved volume accuracy when proliferation variability was high, but diffusion variability was found to only marginally affect tumor volume estimates. Neither proliferation nor diffusion variability significantly affected the spatial distribution accuracy of the simulations. While certain cases of stochastic parameterizations improved volume accuracy, they failed to significantly improve simulation accuracy overall. Both the non-stochastic and stochastic simulations failed to achieve over 75% spatial distribution accuracy, suggesting that the underlying structure of the model fails to capture one or more biological processes that affect tumor growth. Two biological features that are candidates for further investigation are angiogenesis and anisotropy resulting from differences between white and gray matter. Time-dependent proliferation and diffusion terms could be introduced to model angiogenesis, and diffusion weighed imaging (DTI) could be used to differentiate between white and gray matter, which might allow for improved estimates brain anisotropy.
ContributorsAnderies, Barrett James (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Stepien, Tracy (Committee member) / Harrington Bioengineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
E-commerce has rapidly become a mainstay in today's economy, and many websites have built themselves around providing a platform for independent sellers. Sites such as Etsy, Storenvy, Redbubble, and Society6 are increasingly popular options for anyone looking to open their own online store. With this project, I attempted to examine the effects of four different marketing techniques on sales in an online store. I opened a shop on Etsy and tracked sales in connection with promotion through social media, selling products in-person at a convention, holding a holiday tie-in sale, and using price anchoring. Social media accounts were opened on Facebook, Tumblr, and Instagram to promote the shop over the course of the project period, and Etsy's web analytics were used to track which sites directed the most traffic to the shop. I attended a convention in mid-January 2016 where I sold my products and distributed business cards with a discount code to track sales resulting from being at the convention. A holiday sale was held in conjunction with Valentine's Day to look at whether holidays influenced purchases. Lastly, a significantly more expensive product was temporarily put in the shop to see whether it produced a price anchoring effect \u2014 that is, encouraged sales of the less expensive products by making them seem affordable in comparison. While the volume of sales data was too small to draw statistically significant conclusions, the project was a highly instructive experience in the process of opening a small online store. The decision-making steps outlined may be helpful to other students looking to open their own online shop.
ContributorsChen, Candice Elizabeth (Author) / Moore, James (Thesis director) / Sanford, Adriana (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Company X is one of the world's largest manufacturer of semiconductors. The company relies on various suppliers in the U.S. and around the globe for its manufacturing process. The financial health of these suppliers is vital to the continuation of Company X's business without any material interruption. Therefore, it is in Company X's interest to monitor its supplier's financial performance. Company X has a supplier financial health model currently in use. Having been developed prior to watershed events like the Great Recession, the current model may not reflect the significant changes in the economic environment due to these events. Company X wants to know if there is a more accurate model for evaluating supplier health that better indicates business risk. The scope of this project will be limited to a sample of 24 suppliers representative of Company X's supplier base that are public companies. While Company X's suppliers consist of both private and public companies, the used of exclusively public companies ensures that we will have sufficient and appropriate data for the necessary analysis. The goal of this project is to discover if there is a more accurate model for evaluating the financial health of publicly traded suppliers that better indicates business risk. Analyzing this problem will require a comprehensive understanding of various financial health models available and their components. The team will study best practice and academia. This comprehension will allow us to customize a model by incorporating metrics that allows greater accuracy in evaluating supplier financial health in accordance with Company X's values.
ContributorsLi, Tong (Co-author) / Gonzalez, Alexandra (Co-author) / Park, Zoon Beom (Co-author) / Vogelsang, Meridith (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Mike (Committee member) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / School of Accountancy (Contributor) / WPC Graduate Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Abstract
The aim of the research performed was to increase research potential in the field of cell stimulation by developing a method to adhere human neural progenitor cells (hNPC’s) to a sterilized stretchable microelectrode array (SMEA). The two primary objectives of our research were to develop methods of sterilizing the polydimethylsiloxane (PDMS) substrate being used for the SMEA, and to derive a functional procedure for adhering hNPC’s to the PDMS. The proven method of sterilization was to plasma treat the sample and then soak it in 70% ethanol for one hour. The most successful method for cell adhesion was plasma treating the PDMS, followed by treating the surface of the PDMS with 0.01 mg/mL poly-l-lysine (PLL) and 3 µg/cm2 laminin. The development of these methods was an iterative process; as the methods were tested, any problems found with the method were corrected for the next round of testing until a final method was confirmed. Moving forward, the findings will allow for cell behavior to be researched in a unique fashion to better understand the response of adherent cells to physical stimulation by measuring changes in their electrical activity.
The aim of the research performed was to increase research potential in the field of cell stimulation by developing a method to adhere human neural progenitor cells (hNPC’s) to a sterilized stretchable microelectrode array (SMEA). The two primary objectives of our research were to develop methods of sterilizing the polydimethylsiloxane (PDMS) substrate being used for the SMEA, and to derive a functional procedure for adhering hNPC’s to the PDMS. The proven method of sterilization was to plasma treat the sample and then soak it in 70% ethanol for one hour. The most successful method for cell adhesion was plasma treating the PDMS, followed by treating the surface of the PDMS with 0.01 mg/mL poly-l-lysine (PLL) and 3 µg/cm2 laminin. The development of these methods was an iterative process; as the methods were tested, any problems found with the method were corrected for the next round of testing until a final method was confirmed. Moving forward, the findings will allow for cell behavior to be researched in a unique fashion to better understand the response of adherent cells to physical stimulation by measuring changes in their electrical activity.
ContributorsBridgers, Carson (Co-author) / Peterson, Mara (Co-author) / Stabenfeldt, Sarah (Thesis director) / Graudejus, Oliver (Committee member) / Harrington Bioengineering Program (Contributor) / School of Human Evolution and Social Change (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
For our collaborative thesis we explored the US electric utility market and how the Internet of Things technology movement could capture a possible advancement of the current existing grid. Our objective of this project was to successfully understand the market trends in the utility space and identify where a semiconductor manufacturing company, with a focus on IoT technology, could penetrate the market using their products. The methodology used for our research was to conduct industry interviews to formulate common trends in the utility and industrial hardware manufacturer industries. From there, we composed various strategies that The Company should explore. These strategies were backed up using qualitative reasoning and forecasted discounted cash flow and net present value analysis. We confirmed that The Company should use specific silicon microprocessors and microcontrollers that pertained to each of the four devices analytics demand. Along with a silicon strategy, our group believes that there is a strong argument for a data analytics software package by forming strategic partnerships in this space.
ContributorsLlazani, Loris (Co-author) / Ruland, Matthew (Co-author) / Medl, Jordan (Co-author) / Crowe, David (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Mike (Committee member) / Department of Economics (Contributor) / Department of Finance (Contributor) / Department of Supply Chain Management (Contributor) / Department of Information Systems (Contributor) / Hugh Downs School of Human Communication (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
As the IoT (Internet of Things) market continues to grow, Company X needs to find a way to penetrate the market and establish larger market share. The problem with Company X's current strategy and cost structure lies in the fact that the fastest growing portion of the IoT market is microcontrollers (MCUs). As Company X currently holds its focus in manufacturing microprocessors (MPUs), the current manufacturing strategy is not optimal for entering competitively into the MCU space. Within the MCU space, the companies that are competing the best do not utilize such high level manufacturing processes because these low cost products do not demand them. Given that the MCU market is largely untested by Company X and its products would need to be manufactured at increasingly lower costs, it runs the risk of over producing and holding obsolete inventory that is either scrapped or sold at or below cost. In order to eliminate that risk, we will explore alternative manufacturing strategies for Company X's MCU products specifically, which will allow for a more optimal cost structure and ultimately a more profitable Internet of Things Group (IoTG). The IoT MCU ecosystem does not require the high powered technology Company X is currently manufacturing and therefore, Company X loses large margins due to its unnecessary leading technology. Since cash is king, pursuing a fully external model for MCU design and manufacturing processes will generate the highest NPV for Company X. It also will increase Company X's market share, which is extremely important given that every tech company in the world is trying to get its hands into the IoT market. It is possible that in ten to thirty years down the road, Company X can manufacture enough units to keep its products in-house, but this is not feasible in the foreseeable future. For now, Company X should focus on the cost market of MCUs by driving its prices down while maintaining low costs due to the variables of COGS and R&D given in our fully external strategy.
ContributorsKadi, Bengimen (Co-author) / Peterson, Tyler (Co-author) / Langmack, Haley (Co-author) / Quintana, Vince (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / Department of Supply Chain Management (Contributor) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / Department of Marketing (Contributor) / School of Accountancy (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05