Business Intelligence Commons^™

Safeguard Cyberspace In Ransomware Era: Risk Analysis & Cyber Insurance, Li Huang

Electronic Theses & Dissertations (2024 - present)

The increasing frequency and severity of ransomware attacks pose significant challenges for organizational cybersecurity. Fragmentation across disciplines in cyber defense has created practical gaps in the development of the necessary capabilities needed to address rapidly evolving cyber threats. This study explores the impact of ransomware attacks and the evolving role of cyber insurance as a proactive cybersecurity partner. Bridging the gap between actuarial science and cyber risk management, it proposes an interdisciplinary framework that quantifies the impact of ransomware and integrates cyber insurance into cybersecurity strategies.

The primary contribution of this study is methodology. We present a framework that remains …

Surviving And Thriving In The Hybrid Cloud: A Review Of The Current Cloud Computing Landscape, Peter Munsch, Alison Munsch

Journal of International Technology and Information Management

Background and Purpose

Both academic and industry institutions have increasingly migrated essential services to public cloud providers (e.g., Microsoft, AWS, Google) with mixed outcomes. Some industry leaders attempted to fully replace their on-premises data centers with public cloud services, a move not advised without thorough performance and cost analyses (Potel, 2023). Despite some organizations pulling back from the “Cloud First” strategy, the public cloud services market continued to grow, with revenue increasing by approximately 20% year-over-year since 2020 and surpassing half a trillion dollars in 2022 (IDC Worldwide Semiannual Public Cloud Services Tracker, 2H 2022). Cloud technologists suggested that hybrid …

Using Ai Tools To Unmask Sarcasm, Sidra Tehniyath Lnu

Electronic Theses, Projects, and Dissertations

Sarcasm can be identified in newspaper headlines in digital communication, as it is contextual and has low inter- and intra-observer reliability. This research aims to improve sarcastic comment identification using a natural language processing approach, especially the BI-LSTM. The primary concern is to use fine-tuning methods to enhance the ways that strengthen the sarcasm identification rate, which regards the factors that complicate an automatic identification process. The study explores the impact of techniques such as early stopping, optimal loss function selection, and hyperparameter tuning to enhance the model's performance. The research questions are: Q1) How can fine-tuning techniques for BI-LSTM …

Navigating The Landscape: Perceptions, Motivations, And The Journey Of Data Professionals In Implementing Data Processes And Procedures, Willie Rivers

Dissertations

This explanatory study investigates the motivations behind data professionals' willingness or resistance to implementing data policies and procedures, utilizing the Theory of Planned Behavior and Psychological Ownership as frameworks. Through interviews with 21 data professionals, the research explains how factors such as perceived control, ownership, and attitudes towards data processes influence their motivation. The study tests key propositions, including the impact of perceived time burdens, bureaucratic procedures, and organizational expectations on data governance engagement. The findings underscore the critical role of control and access to information in motivating the adoption of data policies, providing insights for organizations aiming to enhance …

Advancing Sustainable Investing: A Deep Learning Model For Multi-Source Stock Prediction, Hongxuan Yu, Tingting Zhang, Murat Kizildag

Journal of Global Business Insights

The burgeoning role of the stock market within the national economy elevates the importance of precise stock price analysis and prediction, a field that has garnered substantial interest in academic research. Stock price fluctuations, influenced by many factors, including company fundamentals, market sentiment, capital flows, industry news, and macroeconomic policies, present a highly dynamic and complex challenge for predictive modeling. Addressing this challenge, our study introduces an innovative method that capitalizes on the synthesis of news text and stock price data for forecasting market movements. We employ GloVe embeddings to capture semantic nuances from news text and integrate them with …

Understanding The Dynamics Of Chatbot Design, Transparency, And Ethical Implications In Customer–Brand Relationship In Hospitality And Tourism Industry, Shakera Parveen

Electronic Theses, Projects, and Dissertations

This culminating experience project investigated the impact of chatbot interactions on customer perceptions, ethical considerations, and transparency within the hospitality and tourism industry. The research questions are: Q1: how do the specific visual and verbal aspects contribute to shaping customer perceptions within the context of chatbot marketing efforts? Q2: what steps can companies take to address and mitigate ethical concerns surrounding interactions with chatbots? Q3: To what extent does the level of transparency and explainability in chatbot interactions influence customer trust and satisfaction? The findings are: Q1: the study revealed that visual and verbal aspects of chatbot design significantly shape …

Experimental Methods In Predicting Market Drift And Other Portfolio Optimization Factors Using Graph Theory, Perry Harrison Zhang

Computer Science Senior Theses

No abstract provided.

Enhancing Customer Support Operations Through Gpt & Q-Learning: A Model Study, Adam Alidra, Bob O'Brien, Dalton Young

SMU Data Science Review

Abstract. “Growth strategies that are purpose-led, customer-centric, experience-driven, data/AI-enabled, and technology-scaled require new mindsets…” (Cornfield, 2021). What can we take from this? Business growth and customer experience are inextricably tied together. Therefore, thriving, as an organization, is dependent on reimagining enterprise operations through modern, scalable data and AI technologies. Our study aims to enhance support operations with emerging AI capabilities, including OpenAI’s LLM models, built on self-attention mechanism transformer architecture, and tailored for business needs through prompt engineering. Our research uses Markov Decision Process and the Q-learning algorithm to evaluate synthetically created support incidents. Through this set of methods, our …

Financial Assessment Of Executive Decision-Making And Risk Management: A Case Study Of Autozone, Carolina Hollis

Honors Theses

This area of investigation pertains to the business organizational structure, executive summary, and risk management decisions of AutoZone within the 2022-2023 fiscal year. This analysis was performed by examining previous financial records, the yearly 10-K report, and various research databases regarding the financial performance of retail auto part companies. Many different sources were used to gather information about AutoZone. AutoZone has an intricate financial system that maintains the going concern by addressing current issues within the financial world to combat problems they could face in the future. These risk factors include inflation, interest rates, energy prices, political climates, and supply …

The Shocking Future Of Artificial Intelligence Across A Variety Of Industries, Collin Elek Boone

Management Undergraduate Honors Theses

Artificial intelligence is a huge phenomenon that has begun to take over worldwide media. The abilities that AI has already proven to be capable of will undoubtedly change the world in a variety of ways. In the future, however, it is possible that AI will have an impact on nearly every sector imaginable. With the success that companies such as OpenAI and Microsoft have already displayed regarding artificial intelligence, it is clear that this software has immense capabilities. The purpose of this study is to conduct research detailing the potential impact of AI on industries within the business sector and …

Balancing Perspectives: Assessing The Integration Of Ai In Academic Support Within Higher Education, Sami Ali

2024 Spring Honors Capstone Projects - Archive

The rapid advancement of A The rapid proliferation of Artificial Intelligence (AI) across various sectors highlights its transformative potential, yet its integration into educational settings raises ethical concerns, particularly regarding its misuse by students. This study conducts a detailed sentiment analysis and systematic literature review to examine the diverse perspectives on AI within education, from both academic and non-academic viewpoints. Utilizing advanced data mining tools to analyze sentiments and trends, this research reveals a complex landscape where optimism about AI’s capabilities is tempered by concerns over ethical implications and potential misuse. The findings contribute to the dialogue on leveraging AI …

Spatiotemporal Negative Inventory Outlier Decomposition For Supply Chain Applications In Consumer-Packaged Goods (Cpg), Hayden Mcdonald

Data Science Undergraduate Honors Theses

Coca-Cola is a popular soft drink brand with sales occurring in every Walmart store across the world, which generates large quantities of data and requires a robust supply chain system. However, the company does not currently have a sophisticated, automated, and/or prescriptive system for detecting where, when, and why inventory outages occur and applying preventative measures to avoid loss of revenue from the absence of inventory on store shelves. This thesis proposes and applies a novel, prescriptive system for this purpose. An inventory outage can be seen as a ‘negative’ statistical outlier in a time series of inventory for an …

Lululemon Strategic Audit, Tessa Mozingo, Olivia Eno, Molly Mitchell, Connor Morrissey, James Eshleman

Honors Program: Senior Projects (Public)

This case study focuses on lululemon, a prominent athleisure apparel company. Our team conducted an audit of the company revolving around strategic business management principles. Our research used publicly available information that included the company’s website, SEC filings, news articles, lululemon’s annual reports, financial statements, IBIS industry reports, and online sources. The goal of this strategic audit is to develop an understanding of lululemon’s business activities and strategies, collect and analyze both internal and external data, and evaluate lululemon based on strategic management concepts.

In this strategic audit, we conduct internal, external, performance, and competitive analyses and an examination of …

Association Between Food Insecurity And Chronic Health Condition Among Adults (18 To 48), Prasanth Reddy Guda

Electronic Theses, Projects, and Dissertations

Uncertainty or limited access to safe food, known as food insecurity, can affect the health and healthcare needs of individuals with several chronic conditions. During 2022, 12.8 percentage (around 17.0 million) of households in the United States experienced food insecurity. The main objective of this study was to examine the Association between food insecurity and chronic health conditions adults. We collected data from NHIS 2022 sample Adults’ Interview (The National Health Interview Survey). The research question asked are: (Q1) Are there certain long term health issues that are more common among adults facing food insecurity age in between the ages …

Impact Of Seasonality On Demand Forecasting Techniques For Small-Scale Food Retailers, Indu Sree Guturu

Electronic Theses, Projects, and Dissertations

This study explores the impact of seasonality on SKU (Stock Keeping Unit) demand forecasting in small-scale food retailers through the analysis of historical sales data, focusing on four key products, labelled: Product A, Product B, Product C & Product D. The research aims to assess the accuracy of different forecasting models in capturing seasonal fluctuations. The research questions are: (Q1) What is the impact of seasonality on SKU demand forecasting in case of small-scale food retailers. (Q2) How does the efficacy of Traditional model compare to alternative technologically driven forecasting techniques? (Q3) To what extent can the impact of seasonality …

Effectiveness Of Cnn-Lstm Models Used For Apple Stock Forecasting, Ethan White

Electronic Theses, Projects, and Dissertations

This culminating experience project investigates the effectiveness of convolutional neural networks mixed with long short-term memory (CNN-LSTM) models, and an ensemble method, extreme gradient boosting (XGBoost), in predicting closing stock prices. This quantitative analysis utilizes recent AAPL stock data from the NASDAQ index. The chosen research questions (RQs) are: RQ1. What are the optimal hyperparameters for CNN-LSTM models in stock price forecasting? RQ2. What is the best architecture for CNN-LSTM models in this context? RQ3. How can ensemble techniques like XGBoost effectively enhance the predictions of CNN-LSTM models for stock price forecasting?

The research questions were answered through a thorough …

An Exploration Of Synergy Evaluation Application Model To Support Implementation On Merger And Acquisition, Jieping Mei

Electronic Theses, Projects, and Dissertations

ABSTRACT

The project focuses on a comprehensive system’s analysis and design of the front-end of the Synergy Evaluation Application Model (SEAM) system for mergers and acquisitions (M&As). The research questions asked are: Q1. How did the SEAM system incorporate the system requirements and design that incorporated the strategic goals and priorities of both the acquirer and the acquiree? Q2. What data sources will the SEAM system rely on, and how does it overcome data integration, automation, visualization challenges? Q3. How will the model identify build in potential synergies, both quantitative and qualitative? The research questions were analyzed through the SEAM …

Data-Driven Forecasting For Effective Demand Management In The E-Commerce Ecosystem, Chirag Pandey

Electronic Theses, Projects, and Dissertations

This culmination project investigated and analyzed the impact of factors influencing sales forecasting models in the e-commerce ecosystem. The research questions are: Q1) To what extent are sales forecasting models in the e-commerce ecosystems influenced by customer demographic variables such as gender, age, and geographic locations? Q2) To what extent are sales forecasting models in the e-commerce ecosystems influenced by product specific factors? The datasets used were from Kaggle, and the Worldometer websites. The findings are: Q1) Individuals aged '55 or over' significantly impact total sales in both the USA and Brazil. Male consumers consistently accounted for a higher proportion …

Experts-Driven Design: A Framework For Measuring Social Influence In Online Social Networks, Shyamala N. Chalakudi, Dildar Hussain Dr., Gnana Bharathy Dr, Dakshinamurthy Kolluru Dr

AMTP Proceedings 2024

In our interconnected and ever-changing social landscape, influence is pivotal in molding individuals’ decisions, perspectives, and behaviors. Measuring Social Influence (SI) in Online Social Networks (OSNs) faces significant gaps, notably needing more integration with human behaviors. Our literature review has unveiled twelve psychological theories rooted in human behavior that are fundamental for assessing SI in OSNs. We have further elucidated how human behaviors interact in shaping the influence potential of social media users through exploratory interviews with experts from consulting, industry, and academia. Our study culminated in developing a novel framework designed to encompass the intricacies that not only nurture …

Meme Stocks, Robinhood, And Wall Street: Retail Investors And Changing Market Dynamics, Nick Murray, Spencer Brosnan

Senior Theses

This thesis explores the rise of retail investors in the wake of the COVID-19 pandemic, and how they have changed the landscape of financial markets. We outline the full timeline of the GME and AMC social media craze that exemplified the power of retail investors in the market and investigate the impact the COVID-19 pandemic had on this group. Through the use of VandaTrack, a software that tracks retail investors’ trading activity, we were able to quantify the impact that retail investors have on US equities and ETFs and compare them to the market as a whole. We utilized this …

Guinness Is The "Pumpkin Spice Latte" Of St. Patrick's Day, Jin-A Choi, Bond Benton, Yi Luo

College of Communication and Media Scholarship and Creative Works

This study by a team of faculty from the Joetta Di Bella and Fred C. Sautter III Center for Strategic Communication in the School of Communication and Media at Montclair State University shows that Guinness was the most-discussed brand on social media leading up to St. Patrick’s Day, and not just in the traditional ways people share how they drink the popular beer brand. The volume of social media conversations related to Guinness beer and St. Patrick’s Day saw a 25% increase. Most social chats exhibited a happy mood as evidenced by a 62% joyful sentiment.

While the Shamrock Shake …

The Role Of Ai, Big Data And Predictive Analytics In Mitigating Unemployment Insurance Fraud, Siddikur Rahman, Md Abu Sayem, Shariar Emon Alve, Md Shahidul Islam, Muhammad Mahmudul Islam, Arifa Ahmed, Mohammed Kamruzzaman

Finance, Economics, and Data Analytics

The fraudulent claims for Unemployment Insurance (UI) have also risen massively in the United States especially during the onset of COVID-19 pandemic with billions of dollars that were lost. These approaches applied formerly in fraud detection and prevention have been challenged by new and advanced fraud systems. For this reason, AI, Big Data and Predictive Analytics are now crucial for improving fraud mitigation in UI programs. The aim of this research is to understand how far AI, Big Data and Predictive Analytics have been utilized, for how effective they are and the barriers they pose in tackling unemployment insurance fraud …

Nike's Product Recommendation System And Incorporation Of Ai, Quinton Truman Lamers

Undergraduate Theses, Professional Papers, and Capstone Artifacts

No abstract provided.

A Comprehensive Study Of Patent Litigation In The Pharmaceutical Sector: Employing Network Theories, Graph Neural Networks, Agent Based Modeling, Bayesian Network Autocorrelation Models, Sreehas Gopinathan

Information Systems & Operations Management Dissertations - Archive

Understanding the dynamics and predictors of patent litigation is crucial in intellectual property management, especially given the competitive edge patents offer companies. Also, patents serve as both legal tools and repositories of innovation. This research delves into the complex world of patent litigation within the pharmaceutical industry, focusing on creating and applying advanced computational models to study litigation propensities. Techniques such as Graph Neural Networks (GNN), Agent-Based Modeling (ABM), and Bayesian Analysis of Network Autocorrelation Models (BANAM) are employed to explore the litigation phenomenon

Consumer Liability And Firm Responsiveness: Evidence From Automobile Recalls, Kashef Abdul Majid, Hari Bapuji

Business

Regulations for product recalls differ internationally. In some countries, the responsibility rests entirely with manufacturers to quickly take corrective measures to ensure consumer safety. In other countries, penalties may also be imposed on consumers who persist in using products that have been recalled. We hypothesize that firm responsiveness (as measured by the time between the product release and the recall) will be higher in markets where product safety regulations that include consumer liability than in markets where product safety regulations focus solely on firms, and that firms that standardize their vehicles across such markets become more responsive in both those …

Artificial Intelligence In Stock Analysis And Portfolio Building, Sean M. Lynch

Honors Theses and Capstones

The emergence of artificial intelligence (AI) investment analysts, often referred to as “robo-advisors”, has the potential to change traditional portfolio management. These systems access vast amounts of data and make portfolio management decisions and stock market predictions. In this study, I compared AI-generated portfolios with portfolios that were created manually. Surprisingly, the human-generated portfolios outperformed the AI portfolios. However, the prompt used in the AI application affects these results. The subset of AI portfolios that were created with specific objectives specified in the prompts outperformed those that were created with more generic prompts. Overall, this research sheds light on the …

College Sports As A Business: How Name, Image, & Likeness Deals Have Altered The Way Colleges Operate, Jake Alexander Macinnis

Honors Theses and Capstones

The introduction of NIL (Name, Image, and Likeness) rights for student-athletes by the NCAA has significantly altered college athletics, enabling athletes to profit from their personal brands. In this thesis, it examines the multifaceted impacts of thesis changes on student athletes, universities, and the broader collegiate sports ecosystem. Some of the key areas explored include the rise of NIL collectives, the influence of lucrative TV deals on conference realignment, and modifications to the transfer portal. The research highlights the lack of transparency in collective payments, raising concerns about regulation amongst colleges and universities on all levels. This complicates recruitment as …

Mechanism Design For Optimizing On-Chain Sell Order In Market Without Market Maker, Nico Pei

CMC Senior Theses

The absence of market makers alters the microstructure of the market. It’s difficult to get exposed to time-weighted prices in markets without market makers. In this paper, we delve into three mechanism designs – discrete gradual dutch auction, continuous gradual dutch auction, and variable rate gradual dutch auction – to study how to execute time-weighted sell orders on blockchain in a market without market makers. To make it simpler for readers to understand, we imagine an example of helping a close friend of Picasso to sell his 100 Picasso paintings in the next 10 years since 1970, with the private …

Key Issues Of Predictive Analytics Implementation: A Sociotechnical Perspective, Leida Chen, Ravi Nath, Nevina Rocco

Journal of International Technology and Information Management

Developing an effective business analytics function within a company has become a crucial component to an organization’s competitive advantage today. Predictive analytics enables an organization to make proactive, data-driven decisions. While companies are increasing their investments in data and analytics technologies, little research effort has been devoted to understanding how to best convert analytics assets into positive business performance. This issue can be best studied from the socio-technical perspective to gain a holistic understanding of the key factors relevant to implementing predictive analytics. Based upon information from structured interviews with information technology and analytics executives of 11 organizations across the …

Early-Warning Prediction For Machine Failures In Automated Industries Using Advanced Machine Learning Techniques, Satnam Singh

Electronic Theses, Projects, and Dissertations

This Culminating Experience Project explores the use of machine learning algorithms to detect machine failure. The research questions are: Q1) How does the quality of input data, including issues such as outliers, and noise, impact the accuracy and reliability of machine failure prediction models in industrial settings? Q2) How does the integration of SMOTE with feature engineering techniques influence the overall performance of machine learning models in detecting and preventing machine failures? Q3) What is the performance of different machine learning algorithms in predicting machine failures, and which algorithm is the most effective? The research findings are: Q1) Effective outlier …