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- All Subjects: Supply Chain
- All Subjects: Social Media
- Creators: Department of Information Systems
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.
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.
The goal of this project is to develop a deeper understanding of how machine learning pertains to the business world and how business professionals can capitalize on its capabilities. It explores the end-to-end process of integrating a machine and the tradeoffs and obstacles to consider. This topic is extremely pertinent today as the advent of big data increases and the use of machine learning and artificial intelligence is expanding across industries and functional roles. The approach I took was to expand on a project I championed as a Microsoft intern where I facilitated the integration of a forecasting machine learning model firsthand into the business. I supplement my findings from the experience with research on machine learning as a disruptive technology. This paper will not delve into the technical aspects of coding a machine model, but rather provide a holistic overview of developing the model from a business perspective. My findings show that, while the advantages of machine learning are large and widespread, a lack of visibility and transparency into the algorithms behind machine learning, the necessity for large amounts of data, and the overall complexity of creating accurate models are all tradeoffs to consider when deciding whether or not machine learning is suitable for a certain objective. The results of this paper are important in order to increase the understanding of any business professional on the capabilities and obstacles of integrating machine learning into their business operations.
This thesis looks at the digitalization process holistically. It recognizes that for a digitalization initiative to be successful, it takes input from multiple departments and experts from diverse backgrounds. This paper will be evaluating the interconnectivity needed between the supply chain and human resources departments to spearhead the creation of a digitalization team. Both sectors must have a firm understanding of the other’s needs, in order to acquire, train, and maintain people who will have the necessary hard and soft skills to develop the digital processes. After conducting extensive research around hiring and training, the researchers identified several best practices that companies can utilize to build a successful digital logistics team. Regarding hiring, companies can improve their current practices by collaborating with universities to create synergy between enterprise needs and college curriculum, as well as utilizing talent acquisition data analytics. They must also employ targeted recruiting strategies to attract high-quality talent and create explicit and attractive job postings. In addition to hiring, companies must also continuously improve their training initiatives to ensure their team’s success. In order to do so, firms should conduct training needs analysis, personalize training using technology, offer non-traditional learning modalities, provide holistic supply chain training, and create a learning culture.
This thesis looks at the digitalization process holistically. It recognizes that for a digitalization initiative to be successful, it takes input from multiple departments and experts from diverse backgrounds. This paper will be evaluating the interconnectivity needed between the supply chain and human resources departments to spearhead the creation of a digitalization team. Both sectors must have a firm understanding of the other’s needs, in order to acquire, train, and maintain people who will have the necessary hard and soft skills to develop the digital processes. After conducting extensive research around hiring and training, the researchers identified several best practices that companies can utilize to build a successful digital logistics team. Regarding hiring, companies can improve their current practices by collaborating with universities to create synergy between enterprise needs and college curriculum, as well as utilizing talent acquisition data analytics. They must also employ targeted recruiting strategies to attract high-quality talent and create explicit and attractive job postings. In addition to hiring, companies must also continuously improve their training initiatives to ensure their team’s success. In order to do so, firms should conduct training needs analysis, personalize training using technology, offer non-traditional learning modalities, provide holistic supply chain training, and create a learning culture.