Creation of a database and Python API to clean, organize, and streamline data collection from an updated Qualtrics survey used to capture applicant information for the Fleischer Scholars Program run by the W. P. Carey UG Admissions Office.
Social media has not always been a traditional form of business strategy for the fashion industry, therefore a study on the role of social media on user engagement for different tiers of fashion brands was conducted. A combination of multiple regression models, ANOVA analysis, and hashtag analysis was done to understand various aspects of the research question. Tests were run against different post types to gain deeper insights on engagement levels and statistical significance. Post frequency and correlation analysis was conducted to understand how followers respond to the content. Overall, reels and carousel media were the most successful in increasing and maintaining user engagement. Prada has the most inactive users and ineffective social media strategies to increase engagement. While they have a high following they are unable to sustain engagement levels through their posts. Whereas, Teddy Fresh, despite being a smaller brand has been successful in maintaining engagement levels through their niche target market. Lastly, SKIMS has the fastest growth rate and has been able to increase following through their high frequency post schedule. For each of the brands, this information can be used to further strategize the marketing content. Social media is dynamic and therefore the approach for curating content will differ; being able to understand which types of posts are doing well is helpful for the brands as they can continue to run analysis when needed.
Social media has not always been a traditional form of business strategy for the fashion industry, therefore a study on the role of social media on user engagement for different tiers of fashion brands was conducted. A combination of multiple regression models, ANOVA analysis, and hashtag analysis was done to understand various aspects of the research question. Tests were run against different post types to gain deeper insights on engagement levels and statistical significance. Post frequency and correlation analysis was conducted to understand how followers respond to the content. Overall, reels and carousel media were the most successful in increasing and maintaining user engagement. Prada has the most inactive users and ineffective social media strategies to increase engagement. While they have a high following they are unable to sustain engagement levels through their posts. Whereas, Teddy Fresh, despite being a smaller brand has been successful in maintaining engagement levels through their niche target market. Lastly, SKIMS has the fastest growth rate and has been able to increase following through their high frequency post schedule. For each of the brands, this information can be used to further strategize the marketing content. Social media is dynamic and therefore the approach for curating content will differ; being able to understand which types of posts are doing well is helpful for the brands as they can continue to run analysis when needed.
As online media, including social media platforms, become the primary and go-to resource for traditional communication, news and the spread of information is more present and accessible to consumers than ever before. This research focuses on analyzing Twitter data on the ongoing Russian-Ukrainian War to understand the significance of social media during this period in comparison to previous conflicts. The significance of social media and political conflict will be examined through Twitter user analysis and sentiment analysis. This case study will conduct sentiment analysis on a random sample of tweets from a given dataset, followed by user analysis and classification methods. The data will explore the implications for understanding public opinion on the conflict, the strengths and limitations of Twitter as a data source, and the next steps for future research. Highlighting the implications of the research findings will allow consumers and political stakeholders to make more informed decisions in the future.
The COVID-19 pandemic’s unprecedented nature caused significant disruptions in the global supply chain industry, resulting in setbacks for supply chain operations. The repercussions of the supply chain challenges impacted various industries. This thesis seeks to investigate the impact of the COVID-19 pandemic on the supply chain industry, with a focus on how disruptions have affected the efficiency and resilience of companies within this sector. Data analytics will be leveraged to analyze these disruptions and improve supply chain operations.
Since its introduction, the use of technology has been rapidly expanding and has been integrated into almost every aspect of daily life. Alongside this growth, there has been an increasingly urgent movement for sustainability and to fight climate change. Because technology is so prevalent in society today, it is important to understand how the use of technology relates to sustainability and climate change. While technology has been beneficial to society, it requires vast amounts of energy to power, which causes significant environmental degradation. On the other hand, technology also has provided useful in reducing carbon emissions and mitigating the effects of climate change. This can be seen in areas such as efficient transportation and logistics systems and smart cities. Thus, technology has the potential to positively impact the environment, but its negative effects must also be reduced. Technology companies also play a large role in the reduction of carbon emissions, as they provide much of the services and technology that we use today. Companies such as Google, Amazon, and Microsoft have all made commitments to sustainability, and it is important that they are held accountable to these commitments. Additionally, as new technologies emerge, their environmental impact must also be calculated. The findings of this thesis show that the main negative impacts of technology come from its energy use and its life cycle, while the main positive impacts come from its indirect effect on production processes, systems, and industries. In the long-term, these indirect positive effects are expected to increase, but the energy demands of technology will also increase. Therefore, managing the energy demands of technology while also allowing for increased efficiency and reductions in carbon emissions is the main challenge that companies face regarding sustainability.