Business Intelligence Commons^™

Company Districts, C.J. Suglia

Fordham Journal of Corporate & Financial Law

Special districts that are owned or controlled by private entities and act almost uniformly like a company town can be dubbed a “company district.” These special districts, similar to historical company towns, have autonomy over the districts, control the local government, and only have to answer to the state government. Historical company towns like Pullman, Illinois and Hershey, Pennsylvania had almost canonical command over the land within their boundaries. Company districts operate their business similar to a company town—in a city that the private entity controls, but do not have employees living on-site. Company districts benefit by being immune to …

Algorithms In Finance: Balancing First Amendment Protections And Regulation, Yusraa Tadj

Fordham Journal of Corporate & Financial Law

As algorithms become a function of decision-making in the financial sector, policymakers, the judiciary, and academics grapple with regulatory questions. With the increased reliance on algorithms in finance, the Securities and Exchange Commission (SEC) proposed a rule to mitigate potential conflicts of interest that can arise out of financial firms using algorithms. Algorithm users, including financial firms, are finding novel ways to protect algorithm use, such as by offering them First Amendment protections.

This Note considers to what extent algorithms can be considered protected speech amidst the complexity of algorithms and relationship within the financial sector. The Note argues that …

Consumer Financial Data And Non-Horizontal Mergers, Linda Jeng, Jon Frost, Elisabeth Noble, Chris Brummer

Fordham Journal of Corporate & Financial Law

This Article explores the potential competitive implications of non-horizontal mergers where they involve extensive consumer data, including consumer financial data. As data become increasingly central to firm strategy, mergers between data-rich firms, while potentially leading to positive outcomes, can also create market power in ways not entirely accounted for by traditional antitrust theory. The Article considers some of these implications. It introduces new metrics for valuing data sets held by merging firms that could help competition authorities evaluate market impacts more effectively. The Article then suggests potential tools to mitigate anti-competitive effects of data-rich mergers. It advocates for further research …

Confronting Indecision, Lane Allison Murray

ENGS 15.11: Design and Education

This course aims to increase students' understanding of indecision and how to confront it. The class provides insight into how the fear of the unknown holds people back from making decisions and about understanding one's values as a means to combat indecision. Students and teachers alike explore instances of indecision and evaluate their own impulses & thought processes by reflecting on the deeper reasons for their choices. Students will increase their comfort level of asking themselves and their peers questions about the values that guide them and, in doing so, strengthen their relationships with themselves and others.

The Past, Present, And Future Of Adaptive Selling: Toward An Integrative Framework, Nawar N. Chaker, Rhett T. Epler, Gabriel Moreno, Dana Amiri, Elizabeth G. Mcdougal

Marketing Faculty Publications

Adaptive selling represents a notable and influential concept in the marketing literature. Despite being discussed in scholarly research and managerial practice for over forty years and mixed findings about its impact, a comprehensive understanding of the construct of adaptive selling remains missing. To remedy this critical knowledge gap, we conduct a comprehensive review of 188 articles across twenty-seven journals. We combine three approaches in our survey of the literature, including a systematic review, a main path analysis, and a bibliographic analysis. Together, this three-prong review offers profound insights into the state of adaptive selling research by (1) mapping the key …

Predicting Crises On The African Frontier Stock Markets With Investor Sentiment Indicators: A Machine Learning Approach, David Korsah, Lord Mensah

Journal of International Technology and Information Management

This study examined the predictive ability of machine learning algorithms in identifying crises within African stock markets. The study employed seven distinct machine-learning models, analyzing historical stock prices from eight stock markets, three major sentiment indicators, and the exchange rates of local currencies against the US dollar, with each data spanning from May 1, 2007, to April 1, 2023. Extreme Gradient Boosting (XGBoost) emerged as the most effective algorithm for predicting crises. Historical stock prices and exchange rates were identified as the most critical features for prediction. On the sentiment side, investors’ perceptions of potential volatility on the S&P 500, …

A Conceptual View Of Data For Decision-Oriented Databases: A Knowledge-Driven Approach, Sung-Kwan Kim, Wenjun Wang, Seunghyun Kim

Journal of International Technology and Information Management

Typical database design goes through three levels of data modeling: conceptual modeling, logical modeling, and physical modeling. In particular, conceptual modeling is important since it captures and documents user data requirements. Conceptual modeling serves as a blueprint for designing a database by defining information content to be included in a database. Presently, decision-oriented databases have no well-accepted conceptual modeling approach to apply. While some use conceptual modeling approaches for transaction-oriented databases such as the ER (Entity-Relationship) model, they are not well-suited for decision-oriented databases. It is hard to map from the ER Model to decision-oriented data models. Others attempt to …

Pedagogy In The Age Of Ai: Exploring Generative Ai For Higher Education, Alison Munsch Phd

Journal of International Technology and Information Management

Generative Artificial Intelligence (AI) presents transformative opportunities for higher education, enabling personalized learning, enhanced student engagement, and efficient pedagogical practices. This tutorial-style article guides educators in integrating generative AI into their classrooms through hands-on activities, practical strategies, and reflective exercises. It explores the capabilities of AI tools such as ChatGPT, their applications across disciplines, and the ethical considerations for their use. By cultivating critical thinking and fostering student readiness for AI-driven futures, this article underscores the transformative potential of generative AI in higher education with an emphasis on the academic areas of business analytics, information systems, and computer science.

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 …

Predicting Global Healthcare Supply Chain Delays: A Machine Learning Approach Leveraging Country-Level Logistics Metrics, Jeevan Sai Gali, Nima Molavi, Sepideh Alavi

Journal of International Technology and Information Management

In global healthcare logistics, ensuring the timely delivery of medical commodities is critical, particularly in low- and middle-income countries characterized by infrastructural limitations and operational uncertainties. This research introduces an advanced, data-driven predictive framework designed to forecast delivery delays by synthesizing granular, internal shipment-level data from the USAID Global Health Supply Chain Program (GHSC-PSM) with external country-level logistics capabilities indicators derived from the World Bank’s Logistics Performance Index (LPI). Rather than relying on retrospective trend analyses, this study employs machine learning algorithms such as Random Forest, XGBoost, Support Vector Machines (SVM), and Multi-Layer Perceptron (MLP) to detect …

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 …

Volume 7 (2024) Artificial Intelligence And Responsibility, Arwa Alnajashi, Danielle Derise, Philip L. Frana, David Mcgraw, Amanda Sawyer, Tatjana Titareva, Raafat Zaini, Allie Zombron

International Journal on Responsibility

The seventh volume of the International Journal on Responsibility (IJR) arrives at a crucial moment in the evolution of artificial intelligence and its integration into our academic and social fabric. As we witness the rapid advancement and deployment of AI systems across various domains, this special issue examines the multifaceted dimensions of responsibility surrounding AI technology, with a particular focus on its role in higher education and broader societal implications.

The articles in this volume contribute to our understanding of responsibility through diverse lenses, from classroom implementation to ethical design considerations. Together, they fulfill IJR’s central mission of exploring “Who …

Role Of Emotional Intelligence In Leadership, Sabrena Craft

M.S. in Leadership

No abstract provided.

Protect Small Businesses: Addressing Security Threats And Insider Risks, Alan Saquella

Publications

Small businesses face unique security challenges that make them highly vulnerable compared to larger organizations. While large companies often have dedicated teams of cybersecurity experts, fraud examiners, investigators and the resources to implement robust security measures, small businesses typically operate with limited budgets and minimal staff, making them prime targets for a variety of internal and external threats.

Striking A Balance: Market Shock & Responses In Automotive Components Manufacturing, Emma Lane Mcgahey

All Theses

This thesis examines the effects of extreme market shocks on supply chain dynamics within the automotive industry. Through an analysis of demand data from an automotive manufacturer to its component suppliers (January 2018 to May 2024), the study investigates the relationship between market shocks and supply chain responses, providing insights into how auto components inventory management handles downstream responses to market shocks. With supporting public data—from FRED, BLS, and the U.S. Census Bureau resources—we explore two primary relationships: the impact of market shocks on the Average Standard Deviation of Demand (SDO) and the effect of demand variability on expedited pricing …

Vale Strategic Audit, Nikolas Mancio

Honors Program: Senior Projects (Public)

The Chinese government is critical in shaping the global mining and metals industry, specifically for companies like Vale S.A., the world’s largest iron ore and nickel producer. China’s dominance as the largest importer of iron ore significantly impacts commodity prices and demand, with its government exerting substantial influence through production quotas, infrastructure spending, and environmental regulations. Vale’s strategic position in the Chinese market is a cornerstone of its operations. It is supported by investments in distribution centers, blending facilities, and high-grade iron ore that align with China’s environmental goals. However, this reliance also makes Vale vulnerable to shifts in Chinese …

Cultural Influences On Leadership Practices: An Examination Of Culturally Diverse Leadership Influences During Organizational Change, Christopher Fairbank

Theses & Dissertations

This study explores the multifaceted culturally diverse influences on leadership practices, specifically in the intricate landscape of organizational change management. The primary objective is to unravel the complexities of leadership dynamics by dissecting the nuances of ethnic leadership styles. The goal of my study is to comprehensively examine how leaders from diverse ethnic backgrounds navigated and exerted influence during organizational change processes. I implemented a robust empirical framework to achieve this objective, strategically incorporating diverse study methods such as surveys, interviews, and organizational case studies. These methodological choices ensured a holistic examination of the interplay between cultural influences and leadership …

An Analysis Of Security Risks Posed By Text-Based Generative Ai And Corporate Security Weaknesses Leading To Data Leaks, Tashya Rakshana Byreddy

Electronic Theses, Projects, and Dissertations

ABSTRACT

Generative AI (GenAI) has become a fundamental part of modern life, influencing how we work, learn, and interact with technology. This project focuses specifically on text-based GenAI, which is widely used for tasks such as information gathering, code improvement, and content creation. Despite its benefits, it presents significant security risks that are often underestimated by users. This project investigates these risks and the corporate security gaps that lead to unintentional data leaks. The project also provides a brief overview of Large Language Models (LLMs), which are based on the deep learning technique known as Transformer architecture, used for performing …

Comparative Assessment Of Machine Learning And Deep Learning Models For Drug Effectiveness Using Sentiment Analysis, Blessing Ogechukwu Nwogu

Electronic Theses, Projects, and Dissertations

In recent years, the proliferation of online patient-generated drug reviews has created a valuable resource for assessing drug effectiveness and patient satisfaction, with sentiment analysis emerging as a powerful tool for extracting insights from this unstructured data.

This culminating research project conducted a comparative analysis of traditional Machine Learning (ML) and Deep Learning (DL) models for assessing drug effectiveness using sentiment analysis of participant reviews. The research aimed to evaluate the performance of Support Vector Machine (SVM), XGBoost, Random Forest, Long Short-Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT) models in this context. This culminating research project addressed …

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 …