Business Intelligence Commons^™

Does Green Pay Less? Global Corporate Bond Evidence On Primary And Secondary Yields, Youssef El Kenawy

Theses and Dissertations

The greenium, or green premium, refers to the lower yield that arises from a bond’s green label, conditional on otherwise identical contractual features and credit risk. In our study, we estimate the greenium by combining causal matching techniques with a neural network–based propensity score approach to construct a closely comparable set of green and conventional bonds. Our empirical framework incorporates issuer fixed effects and currency × issuance-year fixed effects, ensuring that our estimates reflect the impact of the green label itself rather than differences in macro-financial conditions or issuer composition.

Our findings indicate that, once currency-specific issuance-year conditions are absorbed, …

Controversy Associated With Red-Light Traffic Cameras, Alan D. Smith, Anna Abdulmanova

Atlantic Marketing Association Proceedings

No abstract provided.

Decoding Stock Market Movements: The Role Of Unemployment And Volatility, Bipllab Roy, Suparna Bhattacharjee

Northeast Journal of Complex Systems (NEJCS)

This study investigates how unemployment and market volatility interact with stock prices in the Indian context, framing the stock–labour–volatility nexus as a complex adaptive system (CAS) rather than a set of linear, time-invariant relationships. Using verified secondary data on unemployment, India VIX, and NSE stock indices for 2013–2023, we first apply simple and multiple regression as a descriptive baseline. Results show a strong negative association between unemployment and stock prices (R ≈ 0.824, R² ≈ 0.68, p < 0.05), consistent with Keynesian demand-side channels, while the linear VIX–stock relationship is weak and statistically insignificant (R² ≈ 0.07, p > 0.05), consistent with the expectation that volatility operates through non-linear, regime-dependent mechanisms not captured by OLS.

Importantly, we document and transparently disclose critical …

Staffing Decisions At Gabriana Cup, Ana Jimenez, Edith Oblitas, Gabriela Pena Guzman, Adriana Valdivia, Di Wu

Open Educational Resources

Labor costs are one of the largest operational costs in the service industry. Overstaffing increases expenses unnecessarily, and understaffing reduces service quality and customer satisfaction. You are a business operations analyst working for Gabriana Cup, a local coffee shop. The owner has observed that certain hours appear significantly busier than others, creating operational pressure during peak times. The owner would like to reduce unnecessary labor costs and optimize Gabriana Cup's staffing practices.

Staffing schedules are currently based on assumptions rather than historical sales patterns, costing the coffee shop owner through wasted inventory, overstaffing, understaffing, and unnecessary labor costs. The coffee …

Netflix Content Strategy, Di Wu

Open Educational Resources

Netflix operates in a highly competitive streaming market where content strategy directly affects subscriber acquisition, retention, brand positioning, and international growth. A streaming platform cannot simply add more titles indefinitely. Content licensing, production budgets, regional demand, maturity ratings, and genre balance all shape which titles should be prioritized.

You are a content strategy analyst on Netflix's global catalog planning team. Senior management is preparing the next annual content review and wants a data-supported recommendation about how the catalog should be positioned. The team is especially interested in whether Netflix should continue emphasizing movies, expand TV shows more aggressively, prioritize specific …

Smartphone Usage, Charles Howard, Josiah Nkele, Berner Hernandez, Di Wu

Open Educational Resources

A major smartphone company is studying how smartphone usage affects users' stress, productivity, and sleep habits. As part of the product development team, you have been assigned to analyze user behavior data to help create digital wellness features that improve the overall user experience.

The company has collected behavior data from over 50,000 users to better understand how smartphone habits may influence focus, stress, and performance. By exploring this data, the team hopes to discover patterns that can guide healthier digital usage and create a better balance between daily life and technology use.

Senior product managers are considering several possible …

Airbnb Studies, Fatoumata Diabate, Maria Estevez, Jaliah Fabre, Di Wu

Open Educational Resources

In the high-stakes world of short-term rentals, Homeland Sellers serves as an advisory firm for property buyers navigating the global Airbnb market. Historically, investors in the sector have often been led by intuition and gut feeling. That is a dangerous strategy in an increasingly regulated environment. To protect capital, Homeland Sellers is transitioning to a more rigorous, data-driven investment model.

Aspiring hosts face a minefield of financial and legal hurdles. Ignoring local HOA restrictions, fluctuating property tax structures, or strict licensing requirements can create serious risk for net operating income. Beyond regulatory compliance, investors must also account for guest appeal …

Credit Card Analysis, Di Wu

Open Educational Resources

A credit card business unit at a bank is facing a retention problem. More customers are leaving the bank's credit card services, and the retention manager wants to identify which customers are most likely to churn so the bank can intervene before the account is lost.

The bank has customer demographic, relationship, and transaction data for more than 10,000 credit card customers. The data includes age, gender, education, income category, card category, months on book, customer inactivity, contact frequency, credit limit, revolving balance, transaction amount, transaction count, and whether the customer attrited.

You are a business analyst on the bank's …

Investment Decision Making, Muhammad Yaseen, Di Wu

Open Educational Resources

A newly established commercial bank, founded in 2021, has accumulated excess capital reserves. The bank is evaluating long-term investment options to deploy this capital in a way that balances growth and risk. The investment committee is considering two primary strategies: investing in a diversified technology-focused ETF, QQQ, or investing directly in selected individual technology stocks.

The bank's senior management wants capital growth, but it also needs to manage risk exposure. A bank cannot treat an investment decision only as a search for the highest return. It must also consider volatility, concentration risk, liquidity, downside periods, and whether the strategy fits …

German Energy Crisis, Liomar Jimenez, Malachy Mclaughlin, Muhdee Nawab, Mouhamed Sarr, Di Wu

Open Educational Resources

For most of the twentieth century, nuclear energy was the quiet backbone of Germany's electricity system. At its peak in 1997, nuclear power supplied over 31 percent of all electricity generated in the country. It was a reliable, low-cost source that kept German industries competitive across Europe. The country's industrial base, including automakers, chemical producers, and heavy manufacturers, depended on affordable, stable power. Nuclear delivered exactly that.

The political climate changed in the late 1990s. After years of advocacy based on concerns about nuclear waste and reactor safety, the Green Party joined the Social Democrats to form the Red-Green Coalition …

Large Language Models As A Detector For Political Deepfakes, Gracie L. Roberts

Honors Theses

The advancement of generative artificial intelligence has allowed for the creation of highly realistic political deepfakes, posing significant threats to democratic integrity and trust in governmental institutions. This study investigates the effectiveness of Large Language Models (LLMs) as detectors for political deepfakes, specifically exploring their ability to address the transferability, interpretability, and robustness limitations inherent in traditional detection systems.

The research was conducted in four steps including data collection, model selection, experimental testing, and evaluation. Two datasets were used in this study. The first dataset uses 833 fake images from the Political Deepfake Incidents Database (PDID) and 833 real images …

A Forecasting Framework For Distribution Center Capacity Utilization: An Applied Industry Study, Jordan J. Shortt

Data Science Undergraduate Honors Theses

This project develops and evaluates a predictive modeling framework for forecasting distribution center capacity utilization at Company Y, with monthly forecast horizons up to one year. Motivated by the operational challenges of seasonal demand volatility, promotional cycles, and the absence of a formally defined capacity metric, the study first constructs a historical capacity utilization measure from raw warehouse management system data — reconciling item volumes, location dimensions, and utilization factors across all DCs — which serves as the target variable for all modeling work. Four models are developed and evaluated against a naïve seasonal baseline: SARIMA, LightGBM, LSTM, and a …

Developing Tracking Compliance Standards For Inbound Freight: A Data-Driven Industry Application At O’Reilly Automotive, Jackson Endacott

Data Science Undergraduate Honors Theses

Visibility of inbound freight is critical for managing operational efficiency, yet many organizations lack standardized compliance metrics for third-party carriers to uphold, preventing them from utilizing tracking data to make data-driven decisions. During a summer internship with the Transportation Department at O’Reilly Automotive, data inconsistencies were addressed in the Transportation Management System (TMS), and that data was utilized to create tracking compliance standards for third-party carriers. Data populated from various sources within O’Reilly’s TMS was cleaned, validated, and utilized to create a Tracking Scorecard that evaluates message transmission rates, timeliness, and errors. This tool provides actionable insights to improve tracking …

Escaping The Promotion Trap: A Machine Learning Framework For Brand Equity Preservation In Beverage Cpg, Lucas P. Jones

Data Science Undergraduate Honors Theses

When companies acquire beverage brands, they typically value them based on total sales revenue. This traditional approach treats all sales equally over time, whether they are driven by genuine consumer demand or temporary discounts. This is important because while promotions can boost short-term sales, they tend to erode brand value over long periods of time. The measurement problem extends to acquisitions, where buyers lack the tools to distinguish real consumer demand from artificial promotional inflation.

This thesis develops a framework to separate genuine baseline demand from promotional dependence using Nielsen scanner data covering 189 beverage brands across 188,304 weekly observations …

Fintech Innovation And Strategy In Islamic Financial Services In Indonesia: Mapping Trends And Correlations Through Digital Content Analysis, Faisal Binsar S.T., M.M.S.I, Dr., Muhammad Bayu Drs., M.M., Dr., Arif Budiman S.Kom, M.Kom, Lisa Puspitasari S.E., M.Si., Dr.

International Conference on Business and Management Research (ICBMR)

This study explores the impact of fintech innovations and strategies on the operational efficiency and service quality of Islamic financial services in Indonesia, emphasizing their alignment with Islamic financial principles. Using a descriptive quantitative methodology, the research employs digital content analysis of online reviews and social media discussions to uncover key themes. Advanced analytic techniques, including topic modeling with Gibbs Sampling for Dirichlet Multinomial Mixture (GSDMM) and cosine similarity for inter-topic correlation analysis, are applied to data sourced from various digital platforms. The findings reveal three dominant themes: media and community interaction, the performance and services of Islamic financial institutions, …

Investigating Machine Learning As An Anomaly Detection Tool In College Basketball, Eli G. Whitaker

Honors College Theses

Artificial Intelligence (AI) and Machine Learning (ML) are more commonly becoming tools that organizations use to prevent, detect, and deter fraud. Like other organizations, the amount of fraud that the National Collegiate Athletic Association (NCAA) faces is growing at an alarming rate. The primary issue involves athletes that undermine the integrity of their sport by purposefully playing below their true potential as participants in broader betting-related schemes. This thesis focused on Division 1 Men’s Basketball evaluates the viability of using supervised ML to detect anomalies in team performance over games in recent seasons. Anomalies, like red flags in the context …

Reinforcement Learning Approaches For Intelligent Budget Management: A Comparison Of Traditional Budget Model With Q-Learning, And Deep Q-Network Models, Danish Yaqub

Create@State

This study explores the application of Reinforcement Learning (RL) techniques to improve personal budget management and financial decision-making. A traditional rule-based budgeting model is first developed as a baseline approach, where income is allocated to expenses, savings, and debt payments using fixed financial ratios and predefined rules. Although this approach provides a structured framework for financial planning, it lacks adaptability and cannot dynamically optimize decisions when financial conditions change.To address these limitations, two reinforcement learning models—Q-Learning and Deep Q-Network (DQN) are implemented and compared with the traditional budgeting model. In the Q-Learning model, an agent interacts with simulated financial states …

The Convergence Of Big Data And Ai, Prasanna Rajbhandari

Create@State

Big data and artificial intelligence are transforming how organizations analyze information and make decisions. Traditional analytics struggle with large, distributed, and unlabeled datasets. Modern AI systems must adapt quickly, preserve privacy, and extract meaningful insights from complex data environments.

Generative Artificial Intelligence In Professional Selling Course: The Art (And Science) Of Prompt Engineering, Michael Rodriguez, Kevin Trainor, Michael Rodriguez

Atlantic Marketing Association Proceedings

No abstract provided.

Examining Digital Transformation In Maritime Logistics: A Big Data Perspective, Jiyoon An

Atlantic Marketing Association Proceedings

No abstract provided.

Sustainability And Technology Aspects Of Supplier Performance, Alan D. Smith

Atlantic Marketing Association Proceedings

No abstract provided.

Design And Implementation Of A Customer Retention And Voice Ai System For El Nopal, Brando Medina

Undergraduate Theses

This Honors Applied Thesis documents the design and implementation of a live Customer Retention and Voice AI System for El Nopal, a full-service restaurant, to improve customer follow-up, routine communication handling, and cross-channel engagement. The project was developed for El Nopal’s Tyler Center location on Taylorsville Road and focused on building a practical artifact rather than primarily evaluating long-term business outcomes. The completed v1 system consists of three modules: a Customer Retention System centered on a QR-based VIP rewards workflow, a Voice AI System for routine phone support, and a Cross-Channel Integration Layer that connects voice interactions to CRM-based SMS …

Behavioral Biases As Drivers Of Complexity In Stock Markets: An Agent-Based Modeling Approach, David Joseph Dr, Alwin Joseph, Blesson James, Kajal Dass

Northeast Journal of Complex Systems (NEJCS)

By modeling financial systems as Complex Adaptive Systems, this study investigates how behavioral biases influence emergent complexity in stock markets. The study integrates heterogeneous agents, such as rational traders, herding agents, overconfident traders, and anchoring/disposition-driven investors, within a Limit Order Book framework calibrated to both U.S. and Indian market conditions using an Agent-Based Modeling (ABM) approach implemented through the high-fidelity ABIDES simulation environment. Price dynamics, volatility patterns, and liquidity structures were analyzed by Monte Carlo simulation experiments with different behavioral compositions. The results show that behavioral biases cause nonlinear price reactions, produce heavy-tailed return distributions that distort order-book complexity, and …

Crime, Consumers, And Clustering: A Geomarketing Analysis Of Retail Behavior Across Urban Markets, Mark J. Sciuchetti Dr., Jianping Huang

Atlantic Marketing Association Proceedings

No abstract provided.

Deploying Root-Cause Analysis (Rca) Agents: An Implementation Blueprint For Ai-Augmented Marketing Analytics, Seojoon Oh

AMTP Proceedings 2026

Artificial intelligence is widely adopted in marketing, yet many organizations struggle to translate analytical outputs into timely action. This paper presents a practitioner-oriented blueprint for deploying root-cause analysis (RCA) agents within AI-augmented marketing analytics systems. We propose a three-layer reference architecture—semantic data layer, autonomous RCA agent, and action layer—that enables continuous anomaly detection, causal diagnosis, explanation generation, and recommendation delivery. Through simulated e-commerce use cases, we demonstrate how RCA agents identify performance disruptions such as email engagement declines and checkout failures, and translate diagnostic findings into actionable guidance. Beyond system design, we outline organizational workflows, trust-building validation loops, scalability pathways …

Analyzing Big Data-Ai's Impact On Product Innovation And Customer Engagement, Omar Itani, Samer Elhajjar, Ashish Kalra Dr, Manal Yunis Dr.

AMTP Proceedings 2026

In contemporary business landscapes, an organization's capacity to deliver groundbreaking innovative products and foster deep, meaningful engagement with its customers stands as the paramount objective of modern strategic practices. This dual focus not only drives sustainable growth but also fortifies competitive positioning in hyper-competitive markets. Yet, despite the extensive empirical evidence highlighting the transformative benefits of innovation—such as enhanced market share and profitability—and customer engagement—evidenced by loyalty, advocacy, and repeat business—the pivotal role of big data-artificial intelligence (BD-AI) technologies remains strikingly underexplored. In particular, scant attention has been paid to how BD-AI training equips firms to harness these tools effectively …

Bba405-Management Decision Making Syllabus, Di Wu

Open Educational Resources

Syllabus for BBA405 Management Decision Making course.

The Impact Of Targeted Marketing On The Health Of Teenagers In America: Regulatory Safeguards And Implications, Katelyn Overbay

Senior Theses

The rise of digital marketing has transformed how companies engage with consumers, particularly those that fall within the adolescent age group. Teenagers represent a uniquely vulnerable demographic due to the developmental stage they are in, making them especially susceptible to targeted advertising strategies that leverage data analytics, social media algorithms, and behavioral tracking. The aim of this study is to examine the direct and indirect impacts of targeted marketing on the physical, mental, and social health of teenagers in the United States. Drawing on existing literature, case studies, recent lawsuits, and regulatory analysis, the research gathered explores how industries such …

Leveraging Accounting Analytics To Enhance Payroll Accuracy And Fraud Detection In U.S. Public Sector Institutions: A Case Study Approach, Regina Debrah

Beacom School of Business Student Publications

This study explores how accounting analytics can be leveraged to enhance payroll accuracy and improve fraud detection in U.S. public-sector institutions, addressing persistent irregularities amid rising demands for fiscal transparency. The research employs a qualitative design with secondary sources including academic literature, reports, and case studies. The literature identifies successful analytics implementation, such as Treasury OPI’s machine learning for data integration for unemployment claims. These precedents demonstrate direct transferability to payroll’s high volume and rules-based structure. Findings show that analytics significantly reduce improper payments through real-time screening, data integration, and risk prioritization when embedded in workflows. The findings also show …

Ai-Scm Cmm: A Capability Maturity Model For Artificial Intelligence Integration In Supply Chain Management, Lordt Becklines, Omar F. El-Gayar, Patti Brooks, Insu Park

Annual Research Symposium

Artificial intelligence is increasingly deployed in supply chain management, yet many organizations struggle to align adoption efforts with process readiness, data quality, governance, and workforce capabilities, and they still lack validated supply chain specific roadmap for assessing readiness, sequencing investments, and reducing implementation risk. This study develops and evaluates a Capability Maturity Model for Artificial Intelligence Integration in Supply Chain Management to address that gap. Using a design science research approach, the study synthesizes prior literature and practitioner knowledge to define maturity dimensions, capability indicators, and staged progression levels for AI integration in supply chain contexts. The artifact and assessment …