Business Intelligence Commons^™

Impact Of Digital Leadership And Organizational Intelligence On Digital Organizational Transformation: An Empirical Study, İzzet Dedeoğlu, Murat Ak, Filiz Demir, H.Tezcan Uysal

Makara Human Behavior Studies in Asia

Technological and digital developments in the current information age have brought profound transformations to organizations, especially business operations and structures. Digital organizational transformation may be more effectively managed through the combined influence of digital leadership and organizational intelligence. This study examines the impact of digital leadership and organizational intelligence on the digital organizational transformation process, addressing a clear gap in the existing literature, as few studies have empirically investigated these factors together. Data were collected via an online survey from 104 employees of a packaging company in Konya, Türkiye, using a convenience sampling method. The study was designed as an …

Chatgpt Unveiled: Unleashing Ai Magic In Digital Marketing, Jianping Huang, Mark J. Sciuchetti Dr.

Atlantic Marketing Association Proceedings

No abstract provided.

Silicon Rivalry: Understanding The U.S.-China Ai Competition In Southeast Asia, Daniel Christopher Kent

Master in Public Policy Theses

In the rising global strategic competition between China and the United States, artificial intelligence (AI) has taken on a special significance given its potentially transformative impact upon economics and security. Yet while the dialogue around the “AI race” is typically centered on foundational model capability enhancement, application in security contexts, and chip design and manufacturing, there is much less attention paid to its impact on geopolitics. In particular, AI diplomacy—the work of a state to spread its model of AI governance, evangelize its AI companies, and develop critical AI interdependencies and linkages around the world—is far less studied. Nowhere is …

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 …

Exploring The Transformative Effects Of Executive Coaching On Leaders: A Qualitative Study, Dwayne Norman

Theses & Dissertations

This qualitative dissertation examines the transformative effects of executive coaching on leaders’ development, personal growth, decision-making, and overall effectiveness across varied organizational contexts. Grounded in authentic leadership theory and employing a generic qualitative inquiry design, the study investigated how leaders who completed executive coaching describe changes in their behavior and mindset. Eleven experienced leaders participated in semi-structured interviews selected through purposive sampling. Thematic analysis, guided by the cyclical and inductive coding methods of Saldana (2021), generated 187 initial codes, which were distilled into 72 final codes. These final codes were then further refined into twelve categories and four overarching themes: …

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, …

Killing Bali (Part I) A Journey From God’S Paradise To Human Inferno, Pierre Fournié, Dr Pierre Fournié

International Journal of Religious Tourism and Pilgrimage

Tourism is growing and growing fast. Before the pandemic, in 2018, the number of arrivals worldwide reached 1.4 billion arrivals from less than 600 million, twenty years earlier. In many countries, especially in developing ones, tourism is perceived as a unique opportunity, a model of territorial development, possibly a national strategy. In Indonesia, after being elected in 2014, former President Joko Widodo (Jokowi) emphasised the importance of tourism, considered as a strategic sector. It had to become the largest exchange currency contributor to Indonesia’s revenue thus bypassing rubber, coal, oil and gas (Fournié & Dou, 2018).

In the collective awareness …

Transformación De La Gestión Del Talento Humano: Impulsores Legales Y Motivacionales En Cundinamarca (Comercio, Servicios, Agricultura Y Educación), Oswaldo Vanegas Flórez, César Augsuto Giraldo Duque, Benjamín Andrés Manjarrés Zárate, Ana María Balcázar Daza, Juan Román Ávila Meléndez

Ciencias Administrativas, Económicas y Contables

Transformación de la gestión del talento humano. Impulsores legales y motivacionales en Cundinamarca ofrece una visión integral sobre la evolución reciente de la gestión del talento humano en sectores clave del departamento de Cundinamarca, Colombia, a través de un enfoque multidisciplinar, analiza los factores motivacionales, administrativos y legales que inciden en el desempeño y la productividad organizacional. Desde la aplicación de la teoría de la autodeterminación en instituciones de educación superior, hasta los retos administrativos en el sector agrícola, cada capítulo vincula marcos teóricos contemporáneos con estudios de caso locales. Los aportes de autores como Deci y Ryan, Lussier y …

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 …

Scientific Research In Cuba: A General Analysis With A Special Focus On Energy, Agriculture And Technology, Henri Dou, Pierre Fournie

International Journal of Islands Research

An analysis of science in Cuba is carried out using data from the OpenAlex database. Examples are developed: agriculture, rice, technology in relation to the current situation: national food supply, energy and medicine. The predominance of research in biology and medicine and the low commitment in the fields of agriculture, energy, medicinal plants and technology are highlighted. Various development schemes are proposed. The conclusion is that research must be directed towards strategic areas: food and energy, must be less dispersed and that technology must be better taken into account. Three proposals are made: the development of a chemical analysis platform …

When Metal Theft Becomes A Life Safety Crisis, Alan Saquella

Publications

By any traditional measure, scrap and metal theft has long been categorized as a property crime, costly, disruptive, and frustrating, but rarely viewed as an immediate threat to human life. That framing, however, no longer reflects reality. What was once considered a nuisance crime has evolved into something far more consequential, particularly when it intersects with critical infrastructure.

Building on the risks outlined in “Scrap Disposal: When the End of the Supply Chain Becomes a Brand and Public Safety Risk,” by Marcos Fontes, a more serious dimension comes into focus when essential systems are targeted. In these cases, the impact …

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 …

Which Ai For What? An Ai Value Index For Small Business Adoption, Ahesanul Karim Abid

Create@State

Every day, millions of small business owners hear the same advice: use AI to grow your business. But when they look at the options — ChatGPT, Claude, Gemini, Grok, DeepSeek – they face an overwhelming question with no clear answer: which one should I actually use for my specific business need?

This question needs a fact-backed answer. Small businesses make up nearly all U.S. companies, about 99.9%, and employ close to half the country's workforce. Yet they adopt AI at rates 38 percentage points lower than large corporations. The issue is not cost. The issue is that no one has …

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.