Matching Items (22)
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- All Subjects: data
- Creators: Department of Information Systems
Description
Sagebrush Coffee is a small business in Chandler, Arizona that purchases green beans, roasts them in small batches for quality, and ships fresh, gourmet roasted coffee beans across the nation. Deciding which coffee beans to buy and roast is one of the most crucial business decisions Sagebrush and other gourmet coffee roasters face. Further complicating this decision is the fact that coffee is a crop, and like all crops, has a specific growing season and the exact same product cannot usually be ordered from year to year, even if it proves to be successful. The goal of this research is to use data analytics and visualization to help Sagebrush make better purchasing decisions by identifying consumer purchasing trends and providing a recommendation for their portfolio mix. In the end, I found that Latin American coffees are popular with both returning and first-time customers, but a specific country of origin does not appear to be associated with the top coffee producing countries. Additionally, December is a critical month for Sagebrush and Sagebrush should make sure to target the states with the most sales: California, Pennsylvania, and New York. Arizona has growth potential as it is not one of the top three locations, despite the presence of a physical store. Also included in the following report is a portfolio recommendation suggesting how many of each product based on region, processing type, and roast level to carry in inventory.
ContributorsBlue, Jessica Morgan (Author) / Kellso, James (Thesis director) / Davila, Eddie (Committee member) / Department of Information Systems (Contributor) / Economics Program in CLAS (Contributor) / Department of Supply Chain Management (Contributor) / Morrison School of Agribusiness (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This project analyzes the tweets from the 2016 US Presidential Candidates' personal Twitter accounts. The goal is to define distinct patterns and differences between candidates and parties use of social media as a platform. The data spans the period of September 2015 to March 2016, which was during the primary races for the Republicans and Democrats. The overall purpose of this project is to contribute to finding new ways of driving value from social media, in particular Twitter.
ContributorsMortimer, Schuyler Kenneth (Author) / Simon, Alan (Thesis director) / Mousavi, Seyedreza (Committee member) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This report details a prioritization value model that was created for the use of Arizona State University and ASU LightWorks in determining and implementing appropriate sustainability projects for removing greenhouse gas emissions. A thorough review regarding the current project selection process, and an extensive analysis into the desired state of the process was conducted for this paper. The newly developed prioritization model includes multiple attributes that rank and prioritize projects based upon the highest value as determined by criteria set forth by the university. Encompassed within this report are the steps in creating the decision model, as well as the benefits and additional uses of the model for the end user. From the analysis and model created, the end user has the ability to choose carbon neutral projects that better align with the vision of the New American University.
ContributorsAmoroso, Nicholas (Co-author) / Lee, Betty (Co-author) / Brooks, Dan (Thesis director) / Johnson, Travis (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor) / Department of Information Systems (Contributor)
Created2015-05
Description
Data has quickly become a cornerstone of society. Across our daily lives, industry, policy, and more, we are experiencing what can only be called a “data revolution” igniting ferociously. While data is gaining more and more importance, consumers do not fully understand the extent of its use and subsequent capitalization by companies. This paper explores the current climate relating to data security and data privacy. It aims to start a conversation regarding the culture around the sharing and collection of data. We explore aspects of data privacy in four tiers: the current cultural and social perception of data privacy, its relevance in our daily lives, its importance in society’s dialogue. Next, we look at current policy and legislature in place today, focusing primarily on Europe’s established GDPR and the incoming California Consumer Privacy Act, to see what measures are already in place and what measures need to be adopted to mold more of a culture of transparency. Next, we analyze current data privacy regulations and power of regulators like the FTC and SEC to see what tools they have at their disposal to ensure accountability in the tech industry when it comes to how our data is used. Lastly, we look at the potential act of treating and viewing data as an asset, and the implications of doing so in the scope of possible valuation and depreciation techniques. The goal of this paper is to outline initial steps to better understand and regulate data privacy and collection practices. Our goal is to bring this issue to the forefront of conversation in society, so that we may start the first step in the metaphorical marathon of data privacy, with the goal of establishing better data privacy controls and become a more data-conscious society.
ContributorsAnderson, Thomas C (Co-author) / Shafeeva, Zarina (Co-author) / Swiech, Jakub (Co-author) / Marchant, Gary (Thesis director) / Sopha, Matthew (Committee member) / WPC Graduate Programs (Contributor) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Autonomous vehicles (AV) are capable of producing massive amounts of real time and precise data. This data has the ability to present new business possibilities across a vast amount of markets. These possibilities range from simple applications to unprecedented use cases. With this in mind, the three main objectives we sought to accomplish in our thesis were to: 1. Understand if there is monetization potential in autonomous vehicle data 2. Create a financial model of what detailing the viability of AV data monetization 3. Discover how a particular company (Company X) can take advantage of this opportunity, and outline how that company might access this autonomous vehicle data.
ContributorsCarlton, Corrine (Co-author) / Clark, Rachael (Co-author) / Quintana, Alex (Co-author) / Shapiro, Brandon (Co-author) / Sigrist, Austin (Co-author) / Simonson, Mark (Thesis director) / Reber, Kevin (Committee member) / School of Accountancy (Contributor) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The purpose of this project is to provide our client with a tool to mitigate Company X's franchise-wide inventory control problem. The problem stems from the franchises' initial strategy to buy all inventory as customers brought them in without a quantitative way for buyers to evaluate the store's inventory needs. The Excel solution created by our team serves to provide that evaluation for buyers using deseasonalized linear regression to forecast inventory needs for clothing of different sizes and seasons by month. When looking at the provided sales data from 2014-2016, there was a clear seasonal trend, so the appropriate forecasting model was determined by testing 3 models: Triple Exponential Smoothing model, Deseasonalized Simple Linear Regression, and Multiple Linear Regression.The model calculates monthly optimal inventory levels (current period plus future 2 periods of inventory). All of the models were evaluated using the lowest mean absolute error (meaning best fit with the data), and the model with best fit was Deseasonalized Simple Linear Regression, which was then used to build the Excel tool. Buyers can use the Excel tool built with this forecasting model to evaluate whether or not to buy a given item of any size or season. To do this, the model uses the previous year's sales data to forecast optimal inventory level and compares it to the stores' current inventory level. If the current level is less than the optimal level, the cell housing current value will turn green (buy). If the currently level is greater than or equal to optimal level or less than optimal inventory level*1.05, current value will turn yellow (buy only if good quality). If the current level is greater than optimal level*1.05 current level will be red (don't buy). We recommend both stores implement a way of keeping track of how many clothing items held in each bin to keep more accurate inventory count. In addition, the model's utility will be of limited use until both stores' inventories are at a level where they can afford to buy. Therefore, it is in the client's best interest to liquidate stale inventor into store credit or cash In the future, the team would also like to develop a pricing model to better meet the needs of the client's two locations.
ContributorsUribes-Yanez, Diego (Co-author) / Liu, Jessica (Co-author) / Taylor, Todd (Thesis director) / Gentile, Erica (Committee member) / Department of Economics (Contributor) / Department of Information Systems (Contributor) / Department of Marketing (Contributor) / School of International Letters and Cultures (Contributor) / School of Life Sciences (Contributor) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
The goal of this thesis is to conduct a descriptive analysis of the gross domestic product (GDP) sector composition of countries around the world and their respective levels of economic development with consideration of their geographic locations, economic growth over time, and their economic sizes. This analysis will be centered around exploring the differences of the GDP composition of countries at different levels of development, testing the consensus that developed countries tend to be focused on the services sector in comparison to less developed ones, who trend towards focus on the agricultural one. These findings will be primarily attained through use of data interpretation and regression analysis utilizing the statistical software packages of Stata and Excel. Results and analysis are to be supported by powerful data visualizations created in Tableau and the careful examination of said visualizations.
Due to the sheer amount of macro-economic factors and the case specific incidences involved in the determination of a country’s level of economic development, this thesis will focus entirely on the descriptive analysis of the relationship between a country’s GDP sector composition within the agricultural, industrial, and services sectors and their level of economic development measured in GDP per capita. This study will explore the relationship between GDP per capita and geographic regions, growth over time, and economic size as well. These relationships will be used to determine if said factors need to be controlled for when analyzing the relationship between a country’s sector composition and its level of development. A better understanding of what countries look like at all levels of development helps build a complete picture of a what makes a country successful and could be used in future studies that seek to predict economic success based on more and/or separate variables.
Due to the sheer amount of macro-economic factors and the case specific incidences involved in the determination of a country’s level of economic development, this thesis will focus entirely on the descriptive analysis of the relationship between a country’s GDP sector composition within the agricultural, industrial, and services sectors and their level of economic development measured in GDP per capita. This study will explore the relationship between GDP per capita and geographic regions, growth over time, and economic size as well. These relationships will be used to determine if said factors need to be controlled for when analyzing the relationship between a country’s sector composition and its level of development. A better understanding of what countries look like at all levels of development helps build a complete picture of a what makes a country successful and could be used in future studies that seek to predict economic success based on more and/or separate variables.
ContributorsStojsin, Rastko (Author) / Goegan, Brian (Thesis director) / Lopez, Andres Diaz (Committee member) / Department of Economics (Contributor) / Department of Information Systems (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Cognitive technology has been at the forefront of the minds of many technology, government, and business leaders, because of its potential to completely revolutionize their fields. Furthermore, individuals in financial statement auditor roles are especially focused on the impact of cognitive technology because of its potential to eliminate many of the tedious, repetitive tasks involved in their profession. Adopting new technologies that can autonomously collect more data from a broader range of sources, turn the data into business intelligence, and even make decisions based on that data begs the question of whether human roles in accounting will be completely replaced. A partial answer: If the ramifications of past technological advances are any indicator, cognitive technology will replace some human audit operations and grow some new and higher order roles for humans. It will shift the focus of accounting professionals to more complex judgment and analysis.
The next question: What do these changes in the roles and responsibilities look like for the auditors of the future? Cognitive technology will assuredly present new issues for which humans will have to find solutions.
• How will humans be able to test the accuracy and completeness of the decisions derived by cognitive systems?
• If cognitive computing systems rely on supervised learning, what is the most effective way to train systems?
• How will cognitive computing fair in an industry that experiences ever-changing industry regulations?
• Will cognitive technology enhance the quality of audits?
In order to answer these questions and many more, I plan on examining how cognitive technologies evolved into their use today. Based on this historic trajectory, stakeholder interviews, and industry research, I will forecast what auditing jobs may look like in the near future taking into account rapid advances in cognitive computing.
The conclusions forecast a future in auditing that is much more accurate, timely, and pleasant. Cognitive technologies allow auditors to test entire populations of transactions, to tackle audit issues on a more continuous basis, to alleviate the overload of work that occurs after fiscal year-end, and to focus on client interaction.
The next question: What do these changes in the roles and responsibilities look like for the auditors of the future? Cognitive technology will assuredly present new issues for which humans will have to find solutions.
• How will humans be able to test the accuracy and completeness of the decisions derived by cognitive systems?
• If cognitive computing systems rely on supervised learning, what is the most effective way to train systems?
• How will cognitive computing fair in an industry that experiences ever-changing industry regulations?
• Will cognitive technology enhance the quality of audits?
In order to answer these questions and many more, I plan on examining how cognitive technologies evolved into their use today. Based on this historic trajectory, stakeholder interviews, and industry research, I will forecast what auditing jobs may look like in the near future taking into account rapid advances in cognitive computing.
The conclusions forecast a future in auditing that is much more accurate, timely, and pleasant. Cognitive technologies allow auditors to test entire populations of transactions, to tackle audit issues on a more continuous basis, to alleviate the overload of work that occurs after fiscal year-end, and to focus on client interaction.
ContributorsWitkop, David (Author) / Dawson, Gregory (Thesis director) / Munshi, Perseus (Committee member) / School of Accountancy (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
With growing levels of income inequality in the United States, it remains as important as ever to ensure indispensable public services are readily available to all members of society. This paper investigates four forms of public services (schools, libraries, fire stations, and police stations), first by researching the background of these services and their relation to poverty, and then by conducting geospatial and regression analysis. The author uses Esri's ArcGIS Pro software to quantify the proximity to public services from urban American neighborhoods (census tracts in the cities of Phoenix and Chicago). Afterwards, the measures indicating proximity are compared to the socioeconomic statuses of neighborhoods using regression analysis. The results indicate that pure proximity to these four services is not necessarily correlated to socioeconomic status. While the paper does uncover some correlations, such as a relationship between school quality and socioeconomic status, the majority of the findings negate the author's hypothesis and show that, in Phoenix and Chicago, there is not much discrepancy between neighborhoods and the extent to which they are able to access vital government-funded services.
ContributorsNorbury, Adam Charles (Author) / Simon, Alan (Thesis director) / Simon, Phil (Committee member) / Department of Information Systems (Contributor) / Department of English (Contributor) / Department of Economics (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
In the wide world of sports, not all fan bases are created equally—especially in the NBA. Differences in factors like tradition, history, team performance amongst teams make each fan base distinctly unique. This paper will analyze how team performance effects one component of fan behavior: home game attendance. Using win-loss data and home game attendance data for each NBA team from 2001 to 2017, I will construct statistical models to estimate how great of an impact team performance has on each team’s home game attendance. I expect each team’s fan base to respond differently to changes in their team’s win-loss record. This paper will also attempt to quantify other facts that impact attendance at NBA games, including year-to-year changes in team salary expenditures, regional income, and the number of star players playing for the team. Finally, this paper will explore the factors that affect home game attendance for specific games within a given season—things like weather, strength of opponent, and win streaks. Ultimately, the goal of this paper will be to provide NBA business analysts with resources to more precisely anticipate their team’s home game attendance. The ability to understand what motivates the behavior of a fan base is invaluable in creating a marketing strategy that drives fans to the arena. This paper will help to identify teams that are most susceptible to significant fluctuations in attendance and outline alternative strategies to positioning their product offering effectively to fans.
ContributorsSloan, Jacob Marlow (Author) / Lee, Christopher (Thesis director) / Eaton, John (Committee member) / Department of Marketing (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05