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- All Subjects: Social Media
- All Subjects: Data Analytics
- Creators: Department of Information Systems
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 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
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
The United States is in a period of political turmoil and polarization. New technologies have matured over the last ten years, which have transformed an individual’s relationship with society and government. The emergence of these technologies has revolutionized access to both information and misinformation. Skills such as bias recognition and critical thinking are more imperative than in any other time to separate truth from false or misleading information. Meanwhile, education has not evolved with these changes. The average individual is more likely to come to uninformed conclusions and less likely to listen to differing perspectives. Moreover, technology is further complicating and compounding other issues in the political process. All of this is manifesting in division among the American people who elect more polarized politicians who increasingly fail to find avenues for compromise.
In an effort to address these trends, we founded a student organization, The Political Literates, to fight political apathy by delivering political news in an easy to understand and unbiased manner. Inspired by our experience with this organization, we combine our insights with research to paint a new perspective on the state of the American political system.
This thesis analyzes various issues identified through our observations and research, with a heavy emphasis on using examples from the 2016 election. Our focus is how new technologies like data analytics, the Internet, smartphones, and social media are changing politics by driving political and social transformation. We identify and analyze five core issues that have been amplified by new technology, hindering the effectiveness of elections and further increasing political polarization:
● Gerrymandering which skews partisan debate by forcing politicians to pander to ideologically skewed districts.
● Consolidation of media companies which affects the diversity of how news is shared.
● Repeal of the Fairness Doctrine which allowed media to become more partisan.
● The Citizens United Ruling which skews power away from average voters in elections.
● A Failing Education System which does not prepare Americans to be civically engaged and to avoid being swayed by biased or untrue media.
Based on our experiment with the Political Literates and our research, we call for improving how critical thinking and civics is taught in the American education system. Critical thought and civics must be developed pervasively. With this, more people would be able to form more sophisticated views by listening to others to learn rather than win, listening less to irrelevant information, and forming a culture with more engagement in politics. Through this re-enlightenment, many of America’s other problems may evaporate or become more actionable.
In an effort to address these trends, we founded a student organization, The Political Literates, to fight political apathy by delivering political news in an easy to understand and unbiased manner. Inspired by our experience with this organization, we combine our insights with research to paint a new perspective on the state of the American political system.
This thesis analyzes various issues identified through our observations and research, with a heavy emphasis on using examples from the 2016 election. Our focus is how new technologies like data analytics, the Internet, smartphones, and social media are changing politics by driving political and social transformation. We identify and analyze five core issues that have been amplified by new technology, hindering the effectiveness of elections and further increasing political polarization:
● Gerrymandering which skews partisan debate by forcing politicians to pander to ideologically skewed districts.
● Consolidation of media companies which affects the diversity of how news is shared.
● Repeal of the Fairness Doctrine which allowed media to become more partisan.
● The Citizens United Ruling which skews power away from average voters in elections.
● A Failing Education System which does not prepare Americans to be civically engaged and to avoid being swayed by biased or untrue media.
Based on our experiment with the Political Literates and our research, we call for improving how critical thinking and civics is taught in the American education system. Critical thought and civics must be developed pervasively. With this, more people would be able to form more sophisticated views by listening to others to learn rather than win, listening less to irrelevant information, and forming a culture with more engagement in politics. Through this re-enlightenment, many of America’s other problems may evaporate or become more actionable.
ContributorsStenseth, Kyle (Co-author) / Tumas, Trevor (Co-author) / Mokwa, Michael (Thesis director) / Eaton, John (Committee member) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor) / Sandra Day O'Connor College of Law (Contributor) / Watts College of Public Service & Community Solut (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
The prevalence of bots, or automated accounts, on social media is a well-known problem. Some of the ways bots harm social media users include, but are not limited to, spreading misinformation, influencing topic discussions, and dispersing harmful links. Bots have affected the field of disaster relief on social media as well. These bots cause problems such as preventing rescuers from determining credible calls for help, spreading fake news and other malicious content, and generating large amounts of content which burdens rescuers attempting to provide aid in the aftermath of disasters. To address these problems, this research seeks to detect bots participating in disaster event related discussions and increase the recall, or number of bots removed from the network, of Twitter bot detection methods. The removal of these bots will also prevent human users from accidentally interacting with these bot accounts and being manipulated by them. To accomplish this goal, an existing bot detection classification algorithm known as BoostOR was employed. BoostOR is an ensemble learning algorithm originally modeled to increase bot detection recall in a dataset and it has the possibility to solve the social media bot dilemma where there may be several different types of bots in the data. BoostOR was first introduced as an adjustment to existing ensemble classifiers to increase recall. However, after testing the BoostOR algorithm on unobserved datasets, results showed that BoostOR does not perform as expected. This study attempts to improve the BoostOR algorithm by comparing it with a baseline classification algorithm, AdaBoost, and then discussing the intentional differences between the two. Additionally, this study presents the main factors which contribute to the shortcomings of the BoostOR algorithm and proposes a solution to improve it. These recommendations should ensure that the BoostOR algorithm can be applied to new and unobserved datasets in the future.
ContributorsDavis, Matthew William (Author) / Liu, Huan (Thesis director) / Nazer, Tahora H. (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
Description
Predictive analytics have been used in a wide variety of settings, including healthcare,
sports, banking, and other disciplines. We use predictive analytics and modeling to
determine the impact of certain factors that increase the probability of a successful
fourth down conversion in the Power 5 conferences. The logistic regression models
predict the likelihood of going for fourth down with a 64% or more probability based on
2015-17 data obtained from ESPN’s college football API. Offense type though important
but non-measurable was incorporated as a random effect. We found that distance to go,
play type, field position, and week of the season were key leading covariates in
predictability. On average, our model performed as much as 14% better than coaches
in 2018.
sports, banking, and other disciplines. We use predictive analytics and modeling to
determine the impact of certain factors that increase the probability of a successful
fourth down conversion in the Power 5 conferences. The logistic regression models
predict the likelihood of going for fourth down with a 64% or more probability based on
2015-17 data obtained from ESPN’s college football API. Offense type though important
but non-measurable was incorporated as a random effect. We found that distance to go,
play type, field position, and week of the season were key leading covariates in
predictability. On average, our model performed as much as 14% better than coaches
in 2018.
ContributorsBlinkoff, Joshua Ian (Co-author) / Voeller, Michael (Co-author) / Wilson, Jeffrey (Thesis director) / Graham, Scottie (Committee member) / Dean, W.P. Carey School of Business (Contributor) / Department of Information Systems (Contributor) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Predictive analytics have been used in a wide variety of settings, including healthcare, sports, banking, and other disciplines. We use predictive analytics and modeling to determine the impact of certain factors that increase the probability of a successful fourth down conversion in the Power 5 conferences. The logistic regression models predict the likelihood of going for fourth down with a 64% or more probability based on 2015-17 data obtained from ESPN’s college football API. Offense type though important but non-measurable was incorporated as a random effect. We found that distance to go, play type, field position, and week of the season were key leading covariates in predictability. On average, our model performed as much as 14% better than coaches in 2018.
ContributorsVoeller, Michael Jeffrey (Co-author) / Blinkoff, Josh (Co-author) / Wilson, Jeffrey (Thesis director) / Graham, Scottie (Committee member) / Department of Information Systems (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
The objective of this project was the creation of a web app for undergraduate CIS/BDA students which allows them to search for jobs based on criteria that are not always directly available with the average job search engine. This includes technical skills, soft skills, location and industry. This creates a more focused way for these students to search for jobs using an application that also attempts to exclude positions that are looking for very experienced employees. The activities used for this project were chosen in attempt to make as many of the processes as automatable as possible.
This was achieved by first using offline explorer, an application that can download websites, to gather job postings from Dice.com that were searched by a pre-defined list of technical skills. Next came the parsing of the downloaded postings to extract and clean the data that was required and filling a database with that cleaned data. Then the companies were matched up with their corresponding industries. This was done using their NAICS (North American Industry Classification System) codes. The descriptions were then analyzed, and a group of soft skills was chosen based on the results of Word2Vec (a group of models that assists in creating word embeddings). A master table was then created by combining all of the tables in the database. The master table was then filtered down to exclude posts that required too much experience. Lastly, the web app was created using node.js as the back-end. This web app allows the user to choose their desired criteria and navigate through the postings that meet their criteria.
This was achieved by first using offline explorer, an application that can download websites, to gather job postings from Dice.com that were searched by a pre-defined list of technical skills. Next came the parsing of the downloaded postings to extract and clean the data that was required and filling a database with that cleaned data. Then the companies were matched up with their corresponding industries. This was done using their NAICS (North American Industry Classification System) codes. The descriptions were then analyzed, and a group of soft skills was chosen based on the results of Word2Vec (a group of models that assists in creating word embeddings). A master table was then created by combining all of the tables in the database. The master table was then filtered down to exclude posts that required too much experience. Lastly, the web app was created using node.js as the back-end. This web app allows the user to choose their desired criteria and navigate through the postings that meet their criteria.
ContributorsHenry, Alfred (Author) / Darcy, David (Thesis director) / Moser, Kathleen (Committee member) / Department of Information Systems (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
This paper explores the ability to predict yields of soybeans based on genetics and environmental factors. Based on the biology of soybeans, it has been shown that yields are best when soybeans grow within a certain temperature range. The event a soybean is exposed to temperature outside their accepted range is labeled as an instance of stress. Currently, there are few models that use genetic information to predict how crops may respond to stress. Using data provided by an agricultural business, a model was developed that can categorically label soybean varieties by their yield response to stress using genetic data. The model clusters varieties based on their yield production in response to stress. The clustering criteria is based on variance distribution and correlation. A logistic regression is then fitted to identify significant gene markers in varieties with minimal yield variance. Such characteristics provide a probabilistic outlook of how certain varieties will perform when planted in different regions. Given changing global climate conditions, this model demonstrates the potential of using data to efficiently develop and grow crops adjusted to climate changes.
ContributorsDean, Arlen (Co-author) / Ozcan, Ozkan (Co-author) / Travis, Daniel (Co-author) / Gel, Esma (Thesis director) / Armbruster, Dieter (Committee member) / Parry, Sam (Committee member) / Industrial, Systems and Operations Engineering Program (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The consumer decision making process is becoming less complicated, as consumers are turning more and more to social media and peers for reviews and suggestions of new products to purchase. Changes in purchasing trends, along with other external factors, have created a perfect environment for influencer marketing to become more effective for brands than traditional marketing strategies (including television, print, email and radio advertising)—by reaching the right target market with easier ways to track conversion rates and other returns on investment. This thesis looks at the factors that go in to influencer marketing, including why brands utilize this strategy—in terms of budget, returns on investment and best practices for finding the perfect influencers. It also looks at influencer marketing from the view of the influencers themselves. This thesis looks at the spectrum of influence and the motivation and goals of each level—from macro-influencers to micro-influencers and brand advocates. To better understand the research presented in this thesis, a case study of a successful brand, analysis of influencers and a creative project are all presented.
ContributorsOakes, Katherine Danielle (Author) / Montoya, Detra (Thesis director) / Giles, Bret (Committee member) / Department of Supply Chain Management (Contributor) / Department of Marketing (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The concept of branding has been around for centuries, but personal branding is a relatively new concept that has been defined and executed by many public figures. With the rise of technological advancements like social media, professional athletes have ample opportunities to connect with consumers outside of their respective court. Our thesis team conducted research with Dr. John Eaton and Professor Daniel McIntosh to analyze how athletes’ actions and behaviors affect consumers’ opinions about their brand.
We developed multiple surveys that were distributed to Marketing & Business Performance (MKT 300) students at Arizona State University and AWS Mechanical Turk Workers. The goal of obtaining information from both college students and paid survey-takers was to compile a diverse set of opinions regarding how consumers react to athletes’ social media and public behavior. This led us to analyze how consumers interact with athletes on social media platforms based on the sport they play and consequences of their actions. After examining our consumer research, interviewing executives in the legal background, and talking to some of the university’s top-prospective athletes to gain different viewpoints, we created consumer and athlete categories.
We established six main consumer categories and six main athlete social media strategy personas in order to create social media strategy recommendations. With this information, athletes have the opportunity to develop well-thought out social media strategies that are more tailored to their fan base(s). Athletes must be cognizant of how the content on their social media accounts and their public actions will affect consumers’ perceptions about who they are and their personal brand.
We developed multiple surveys that were distributed to Marketing & Business Performance (MKT 300) students at Arizona State University and AWS Mechanical Turk Workers. The goal of obtaining information from both college students and paid survey-takers was to compile a diverse set of opinions regarding how consumers react to athletes’ social media and public behavior. This led us to analyze how consumers interact with athletes on social media platforms based on the sport they play and consequences of their actions. After examining our consumer research, interviewing executives in the legal background, and talking to some of the university’s top-prospective athletes to gain different viewpoints, we created consumer and athlete categories.
We established six main consumer categories and six main athlete social media strategy personas in order to create social media strategy recommendations. With this information, athletes have the opportunity to develop well-thought out social media strategies that are more tailored to their fan base(s). Athletes must be cognizant of how the content on their social media accounts and their public actions will affect consumers’ perceptions about who they are and their personal brand.
ContributorsRishwain, Demetra Nicole (Co-author) / Delgado, Samantha (Co-author) / Sminkey, Marie (Co-author) / Eaton, John (Thesis director) / McIntosh, Daniel (Committee member) / Department of Marketing (Contributor, Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05