Business Analytics Commons^™

Ai-Driven Predictive Analysis Of Airline Profitability Under Macroeconomic And Sustainable Aviation Fuels, Shivika Singh

Discovery Day - Daytona Beach

This study presents an AI-enhanced predictive model to assess airline profitability under the combined influence of macroeconomic trends and the adoption of sustainable aviation fuel (SAF). By including economic indicators such as GDP growth, inflation rates, and fuel price volatility with airline operational data, including ticket prices and fuel costs, the model simulates profitability across multiple carriers. Scenario-based analyses, encompassing optimistic, moderate, and pessimistic projections, illustrate different financial sensitivities between low-cost and legacy airlines.

Predicting Passenger Demand On National Flights Departing From Hartsfield-Jackson Atlanta International Airport (Atl) In 2024, Brooklyn Gossett, Ana Yu Wen

Discovery Day - Daytona Beach

The aviation industry relies heavily on accurate demand forecasting to guide critical decisions regarding route planning, capacity management, and pricing strategy. Misjudging passenger demand can result in significant revenue loss and operational inefficiency, making it essential for airlines and analysts to identify the key drivers of flight patronage. This study investigates the factors that most significantly predict the number of passengers on domestic flights departing from Hartsfield-Jackson Atlanta International Airport (ATL) during the 2024 calendar year. Using passenger and route data sourced from the Bureau of Transportation Statistics (BTS) and the U.S. Department of Transportation (DOT), a multiple regression analysis …

Ai-Driven Financial Forecasting, Risk Assessment And Decision Making In Start Up Enterprises, Ntando S. Sibanda, Petronella Muziti

African Conference on Information Systems and Technology

Start-up enterprises operate in uncertain and rapidly changing financial environments characterized by volatile cash flows, limited historical data, and high exposure to financial risk. Conventional financial forecasting and risk assessment methods are often static and assumption-driven, limiting their effectiveness in supporting managerial decision making. Within the Decision Support Systems (DSS) domain, this study analyses the effect of AI-driven DSS on financial forecasting, risk assessment and managerial decision making among start-up enterprises in the Harare Central Business District, Zimbabwe. The study adopts a quantitative approach using structured questionnaires administered to founders, financial managers, and senior decision makers. Data were analysed using …

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

Atlantic Marketing Association Proceedings

No abstract provided.

Generative Ai’S (Genai) Impact On Sales Performance: An Empirical Study On Business-To-Business Sales Process, Michael Rodriguez, Michael Rodriguez, Mike Krush, Dawn Deeter-Schmelz

Atlantic Marketing Association Proceedings

No abstract provided.

Development Of Artificial Intelligence (Ai) Utilization Scale In Business To Business (B2b) Sales Process And Conceptual Model: Exploratory Study, Michael Rodriguez

Atlantic Marketing Association Proceedings

No abstract provided.

Marketing Orientation: A Longitudinal Study Of Community Banks, Stephen Carlson, Gerald Sullivan, Susanna Warnock, Margaret Ryder

Atlantic Marketing Association Proceedings

No abstract provided.

Navigating Environmental, Social, And Governance (Esg) Risks In The Online Food Delivery Platforms: Using Sentiment Analysis Approach, Maryam Mahdikhani, Jiyoon An, Arim Park

Atlantic Marketing Association Proceedings

No abstract provided.

Using Location Tracking And Gis Tools To Investigate Customers’ Cluster Shopping Behavior, Mark J. Sciuchetti Jr, Jianping Huang, Brent Cunningham

Atlantic Marketing Association Proceedings

No abstract provided.

Credit Card Indebtedness During The Covid-19 Pandemic, Alan D. Smith

Atlantic Marketing Association Proceedings

No abstract provided.

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, …

Analysis The Influence Of Product Attributes On Purchase Decision In Choosing A Smartphone Apple Or Samsung., Arumadina Islamiq, Indrawati Indrawati

International Conference on Business and Management Research (ICBMR)

Smartphones are currently a very important and necessary means of communication for the public. Two of the most popular smartphone brands are Samsung and Apple. The purpose of this study is to measure respondents' assessments of product attribute variables (quality, features, brand, design style, and support services) in purchase decisions.

This research is descriptive. It uses quantitative methods. This study examines two variables in detail, namely product attributes (X) as the independent variable and Purchase Decision (Y) as the dependent variable. The respondents in this study consisted of all consumers who currently use Samsung or Apple smartphone products, with 400 …

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.

Global Apparel Companies Adjusting Their Supply Chain Strategies To Post-Covid-19, Alan D. Smith

Atlantic Marketing Association Proceedings

No abstract provided.

Geomarketing: Revolutionizing Market Strategies And Enhancing Spatial Decision-Making Through Ai-Driven Insights, Mark J. Sciuchetti Dr., Jianping Huang

Atlantic Marketing Association Proceedings

No abstract provided.

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

Atlantic Marketing Association Proceedings

No abstract provided.

Marketing The Future Of Radiology: How Ai Integration Enhances Diagnostic Precision And Streamlines Clinical Workflows, Monisha Gupta, Jordan -. Watts Ms, Alberto Coustasse Hencke Dr.

Atlantic Marketing Association Proceedings

No abstract provided.

Face Presence In User-Generated Photos And Its Effect On Review Helpfulness, Anh Dang, Bridget Nichols, Mark Nichols

Atlantic Marketing Association Proceedings

No abstract provided.

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.

The Impact Of Big Data Analytics Powered By Artificial Intelligence On Supply Chain Resilience And Corporate Social Responsibility, John Dickens, Hasan Uvet, Antonina Bauman, Sally Hiott

Atlantic Marketing Association Proceedings

No abstract provided.

Localized Nuclear Utility For Erau Environmental Improvement, Timothy Schroeder, Jacob Blanton, Christopher Anderson

Sustainability Conference

The reliance of Embry Riddle Aeronautical University (ERAU) on energy supplied by Florida Power & Light (FPL), which currently draws large amounts of power from fossil fuel sources, is continuously suboptimal both environmentally and financially. This study proposes assessing the feasibility of deploying a campus scale nuclear reactor tailored to ERAU’s power consumption profile. This serves as a path toward reducing greenhouse gas emissions and limiting dependence on external fossil fuel generation. The methods used are primarily literature review(s), and numerical analysis on comparable use cases. The literature reviews focused on design theory and yield of nuclear reactors on a …

Time Travel Validation For Time Series Data, Thomas Tiahrt

Mountain Plains Business Conference

Model validation is necessary to evaluate the predictive effectiveness of time series forecasts. The standard validation method is to start at the earliest point in the data, train on two of the minimum meaningful number of periods or cycles of periods (i.e., two or more blocks) then test on the next block. Subsequent models are tested by extending the training period one block at a time and testing on the following block. Alternatively, the last block can be substituted for the following block. Other forward progressions can be used including forms of cross validation. In any case, the forecaster chooses …

When Generative Ai Helps Too Much: Engagement, Learning, And Student Outcomes In Business Statistics, Bruce A. Reinig

Mountain Plains Business Conference

After two decades of teaching business statistics, first in person, then online, and now in the era of generative AI, I have observed substantial shifts in how students approach learning. Many rely on ChatGPT and similar tools to produce assignment answers, sometimes at the expense of genuine understanding, which may lead to lower exam scores. Using LMS analytics, I track students’ engagement with video lectures and find this behavior more predictive of exam success than assignment completion. This presentation reports on an ongoing observational study and seeks feedback to strengthen its design and effectiveness in supporting authentic student learning.

Sentiment Analysis Of Public Commentary On U.S. Farm Bills, Frank Tenkorang, Fletcher Ziwoya

Mountain Plains Business Conference

This study applies sentiment analysis to public commentary on U.S. farm bills to assess patterns of support and opposition. Using Reddit posts from relevant subreddits, we employed two natural language processing models, VADER and RoBERTa, to evaluate sentiment over time. While both models yielded differing sentiment distributions, they revealed increased public engagement during farm bill years, particularly in 2018. Most sentiments were neutral, but negative opinions outweighed positive ones. Posts with stronger sentiments tended to attract more user interaction, especially controversial ones. These findings highlight the polarized yet active public discourse surrounding agricultural policy and the potential for data-driven policy …

Applying Detective Analytics For Mitigating Financial Crime In An Organisation, Nontobeko Mlambo, Tiko Iyamu

African Conference on Information Systems and Technology

Despite the growing popularity of detective analytics, it is consistently challenging to understand the factors that influence the adoption. This is increasingly discouraging organisations, even though the tool is important for tracing, tracking, and mitigating financial crime. This study applied concepts of ‘follow the actors’ and shifting negotiation of actor-network theory (ANT) to gain a better understanding of the factors that influence the adoption of detective analytics in a qualitative case study using a South African-based financial organisation. From the analysis, integration of analytics tools, integration of detective analytics with other systems and processes, development of a template for each …

Discovering Prompt Engineering: A Qualitative Study Of Nonexpert Teachers' Interactions With Chatgpt, Katherine Carl, Christopher A. Dignam

Research Days

Discussions of the use of artificial intelligence (AI) have become ubiquitous in research, industry, and education. Prompt engineering has emerged as a valuable skill desired by employers, and students have begun to express interest in learning effective ways of interacting with Generative AI. While much research has been done to formalize and optimize the process of prompt engineering, few studies have investigated the use of AI by nonexperts, including nonexpert teachers. In this study, we use qualitative analysis to characterize the process by which nonexpert teachers engage with and learn from ChatGPT as they engage with it as co-creators to …

A Machine-Learning Tool-Supported Methodology For Nonprofit Donor Analysis, Corbin Weiss

Campus Research Month

We developed a machine-learning tool-supported methodology for modeling the nonprofit donor relationship. This approach was demonstrated in the case of a US-based nonprofit. Conclusions were drawn from this example and tool-support provided for use by other nonprofits.