Management Information Systems Commons^™

Case Study: Feasibility Of Creating A Simulated Mobile Data Center For Cross-Disciplinary Academic Programs, Stanley Mierzwa, Christoper J. Schultz, Iassen Christov, Michael Fagioli, Thomas Ikeda, Reinaldo Jaramillo, Giolian Sanagustin

Center for Cybersecurity

This case study examines the potential to envision, create, and deploy a simulated mobile micro data center solution that can be easily replicated and transported between locations and educational settings. The coined term for this solution is the Mobile AI-Centered Data Center (Mobile ACDC), which provides students with a platform to construct, in a hands-on fashion, such a solution and navigate the product to gain greater competencies and understanding of the components found in a data center. Instructor and student feedback assessments from the pilot classroom modules and laboratory experiential learning activities indicate that such a solution helps to improve …

Wearable Technology For Depression Assessment: A Scoping Review Of Datasets, Ml Tlachac, Hayley K. Elsbree, Michael V. Heinz

Information Systems and Analytics Department Faculty Journal Articles

As wearable technology continues to develop, wearable devices are becoming more common and being increasingly used to collect datasets for depression assessment. Documenting the collection procedures, recruitment strategies, and demographics of these datasets is important to allow for synthesis across the datasets and uncover common limitations. As such, in this scoping review, we identify 80 observational datasets collected by wearable devices through the start of 2025 that can be used for depression assessment. Of the 80 datasets, 50% used an actigraph and 47.5% used other wristbands. These wearable devices were used to collect activity, sleep, and heart rate for 87.5%, …

Trends In Non-Profit Cybersecurity: Analyzing Three Years Of Incident Data From The Npcir, Stanley Mierzwa, Joanna Paliszkiewicz, Edyta Skarzyńska

Center for Cybersecurity

This study analyzes cyberattack trends targeting non-profit organizations using longitudinal data collected over a three-year period within the Non-Profit Cybersecurity Incident Repository (NPCIR). Developed through a National Security Agency Center of Academic Excellence in Cyber Defense (NSA CAE-CD) designated center, the NPCIR applies an open-source intelligence (OSINT) methodology to systematically document cybersecurity incidents affecting the global non-profit sector. This study examines attack types, threat actor characteristics, sectoral distribution, and cybersecurity impacts using the Confidentiality–Integrity–Availability (CIA) triad framework. The results indicate that availability-related incidents, particularly ransomware and distributed denial-of-service (DDoS) attacks, constitute the most prevalent threats, while confidentiality breaches remain highly …

Trust And Pre-Employment Background Checks When Onboarding And Maintaining Information Security And Cybersecurity Staff, Stanley Mierzwa

Center for Cybersecurity

The realm of trust is broad and can include many facets that are difficult to capture and catalog. In relation to the work roles of information security and cybersecurity, the intersection of trust in human resource management is critical and an evolving area within most modern organizations, in almost any sector, and of any size. A foundational element of trust is fundamental to effective mission and work roles in information security and cybersecurity, as well as to every employee tasked with contributing to the security of an organization’s assets. This chapter will include sections on the role trust can and …

Algorithmic Resilience Memory: Designing Agentic Ai Systems For Organizational Learning And Climate-Crisis Adaptation, Harsha Sammangi, Aditya Jagatha, Navyasri Maddukuri

Research & Publications

Climate disruption has become a persistent organizational condition rather than an episodic event, yet most information systems designed to support organizational resilience treat each disruption as an isolated incident. Existing digital resilience platforms, disaster recovery systems, and AI-driven decision support tools lack the capacity to accumulate, encode, and reuse organizational knowledge across successive climate-related crises. This paper introduces Algorithmic Resilience Memory (ARM), a novel IS construct defined as an AI-enabled organizational capability through which agentic AI systems sense climate-related disruptions, encode prior organizational responses, preserve decision rationale, generate contextually adaptive recommendations, and reconfigure future actions through structured outcome feedback. Drawing …

Strategic Audit - Lululemon, Jared M. Loos, David Goldsmith, Madelyn Martin, Grant Taylor, Carson Downs

Honors Program: Senior Projects (Public)

No abstract provided.

Information Sharing, Quality Management, And Firm Performance: The Mediating Role Of Supply Chain Agility, Aamir Rashid, Rizwana Rasheed, Syed Babar Ali

Publications and Research

The fashion industry’s business is becoming increasingly complicated and active. This industry is expected to be highly competitive, particularly in the retail sector. Therefore, this research aims to examine the impact of supply chain information sharing and quality management on firm performance, with supply chain agility as a mediating variable, in the Asian fashion industry. A total of 169 participants from the fashion sector in a developing country were surveyed. The proposed hypotheses were examined using a quantitative approach, employing Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS to assess and validate the measurement model. The results indicate that …

Bias In Generative Ai: Amplified Stereotypes And Their Impact On Decision-Making, Jaymo Kim, Mi Zhou, Vibhanshu Abhishek, Tim Derdenger, Kannan Srinivasan

AMTP Proceedings 2026

This study examines bias in generative AI through an analysis of approximately 8,000 occupational portraits created by Midjourney, Stable Diffusion, and DALL·E 2. We document significant underrepresentation of women and Black individuals compared to real-world benchmarks. The research identifies two primary manifestations of bias: systematic gender and racial disparities, and subtle biases in facial expressions that influence perceptions of competence and trustworthiness. Through an iterative "Creative Lab" involving a fictional brand, we employ a three-phase experimental design to test whether AI disclosure labels—as proposed in the AI Disclosure Act of 2023—can mitigate the impact of these biases on consumer evaluations. …

Research Days: Case Study: Creation Of A Student-Driven Radio Talk Show To Answer Cybersecurity Questions And Concerns To Boost Power Skills, Joel Leiva, Anthony Bayate, David Abiandu, Keith Fernandez

Center for Cybersecurity

Power skills are essential in any professional career. Oftentimes, college students don’t feel prepared enough to enter the workforce. Having good power skills in a group can greatly increase production and efficiency. This case study aims to develop these power skills in a group of college students through the creation of a student-driven radio talk show answering cybersecurity questions and concerns. A qualitative approach was used via the creation of the C.Y.B.E.R. radio show. This show enhanced the participants’ power skills such as collaboration, teamwork, and communication skills. The findings from this case study prove the alternate hypothesis of boosting …

Strategic Economic Evaluation Of Microsoft's Post-Acquisition Gaming Portfolio, Brett Hancock, Daniela Orta Castaneda, Travis Stevens, M. Affan Badar

Symposium Projects

Microsoft Gaming is a gaming and entertainment division of Microsoft. In 2023 Microsoft bought Activision Blizzard, another industry leader. Rapidly changing industry and increased operation costs have created challenges. Sound financial analysis required to maintain company profitability and health. The purpose of this study is to analyze the economic data to recommend potential areas of investment and consolidation.

Reconstruction Of Information System Acceptance Model In The Era Of Integrated Artificial Intelligence: A Systematic Literature Review, Ilham, Merlin Apriliyanti

Library Philosophy and Practice (e-journal)

This study aims to explain the rapid development of Artificial Intelligence (AI) which has driven significant transformations in the development and use of information systems. However, most classical information system acceptance models, such as the Technology Acceptance Model (TAM) and  (UTAUT), have not been able to fully explain the unique characteristics of AI-based systems that are autonomous, adaptive, and complex. This study aims to reconstruct the information system acceptance model in the era of integrated AI through a Systematic Literature Review (SLR) approach. This study was conducted using the PRISMA protocol on 130 leading scientific articles indexed by Scopus and …

Securing U.S. Leadership In Agentic Ai Literacy And Adoption: U.S. Vs Chinese Government Policies And Initiatives, Satyadhar Joshi

Harrisburg University Other Works

This paper conducts a comparative analysis of U.S. and Chinese frameworks for AI literacy and adoption, with focus on agentic AI and Artificial General Intelligence (AGI) systems capable of autonomous reasoning and execution. We examine national policies, educational integration, governance structures, and technological roadmaps, employing both qualitative review and quantitative modeling. Mathematical formulations include multi-dimensional literacy scoring, Bass diffusion models for adoption dynamics, risk assessment functions, regulatory effectiveness indices, competitiveness metrics, and optimization frameworks for resource allocation. Our analysis reveals divergent paradigms: the U.S. Favors decentralized, innovation-driven approaches with emphasis on interoperability and public-private collaboration; while China pursues centralized, state-led …

The Impact Of Value Homophily, Rational And Emotional Persuasion On Information Passing Of Social Media Advertisements: A Model Comparison Approach, Gloria Hui Wen Liu, Cecil Eng Huang Chua, Neil Chueh An Lee, Jenny Hua Jen Wu

Business and Information Technology Faculty Research & Creative Works

Increasingly, businesses collaborate with influencers and content creators (the source) to advertise on social media, with social media users being exposed to an environment saturated with unsolicited advertisements. With social contacts remaining the most trusted advertisement sources, users' passing of such advertisements helps their dissemination and creates a specific kind of electronic word of mouth called information passing. Information passing involves users forwarding advertisements about products/services to someone else. Prior studies have found at least three factors influence information passing, including value homophily (similarity with the source as perceived by users), rational appeal (information about how a product can meet …

Design Principles For Customer-Engaging Digital Service Systems: An Action Research Study, Keng Leng Siau, Xiaofeng Chen, Xin Tan

Research Collection School Of Computing and Information Systems

Digital services represent a business approach employed by organizations to operate in the digital environment. However, systematic development guidelines for developing quality digital service systems are lacking in the literature. The authors identified four general challenges for developing and implementing customer-engaging digital service systems (CEDSS). By employing the method of canonical action research in a digital service system project, they derived 10 design principles for developing high-quality CEDSS. They empirically evaluated the design principles in the development project and through follow-up focus group sessions. The design principles provide applicable and actionable guidelines for the development of CEDSS.

Probabilistic Deep Learning For Traffic Density Prediction, Pedro Cesar Lopes Gerum, Andrew Reed Benton, Melike Baykal-Gursoy

Business Faculty Publications

Real time, accurate predictions of recurrent and nonrecurrent traffic congestion are essential for optimizing transportation systems and ensuring a smooth user experience. Traditional models often focus on long-term point estimates, limiting their use in scenarios requiring short-term predictions or probabilistic assessments (e.g., traffic signal optimization, dynamic tolling, and emergency response). This study explores probabilistic deep learning (DL) for real time traffic density distribution prediction. This study demonstrates that an adapted multi-quantile recurrent neural network (MQRNN), termed MQRNN-monotonic, outperforms traditional time series methods, particularly when handling nonrecurrent disruptions. A novel loss function is introduced to address quantile crossing issues, ensuring valid …

8-Page Zine Extra Credit Assignment, Ricaute Rogers

Open Educational Resources

This is an 8-page minizine extra credit assignment that put into practice the concepts learned in CIS100.

Ai-Based Requirements Analysis Assistant That Applies Explicit Knowledge And Includes Humans In The Loop, Steven Alter

Business Analytics and Information Systems

This exploratory paper builds on the EMMSAD 2024 paper “Could a Large Language Model Contribute Significantly to Requirements Analysis?” Eight versions of each of three LLM prompts (for system structure, analysis, and recommendations) were applied to three 3000+ word case studies. Those versions expressed different “treatments” including a control with no RAG augmentation, a version with RAG augmentation based on an analysis template used by MBA and EMBA students, and six other versions based on theoretical approaches such as activity theory, a BPM design space, work system principles, and so on. The LLM responses were somewhat reliable for summarizing system …

Histodx: Revolutionizing Breast Cancer Diagnosis Through Advanced Imaging Techniques, Wishal Arshad, Thhreem Masrood, H. M. Shahzhad, Hassan A. Ahmed, Syed Hamza Ahmed, Hafiz Muhammad Tayyab Khushi

Business Faculty Publications

Breast cancer is the second leading cause of mortality among women worldwide, highlighting the need for efficient histopathology-based screening methods for early diagnosis. This study introduces HistoDX, a deep learning framework to classify Invasive Ductal Carcinoma (IDC) using 277,524 histopathology image patches ( 50×50 pixels) from Paul Mooney’s IDC dataset on Kaggle, comprising No Cancer and IDC(+) classes. HistoDX employs a preprocessing pipeline with normalization, data augmentation, and class balancing via oversampling and weighted loss to address the class imbalance. A customized convolutional neural network, built on EfficientNetV2-B3 with additional layers, achieves 97% accuracy and a 0.91 ROC-AUC score on …

Case Study & Lessons Learned: Creation And Pilot Of A Regional Small Business Accelerator And Cybersecurity Assessment Program, Stanley Mierzwa, Randall D. Pinkett, Willie Mae Veasey, Debra Price

Center for Cybersecurity

Startup companies and originated small businesses are an essential aspect of our nation’s economy, contributing to many organizations that aim, in some cases, to become larger enterprises. As a small business is in the mode of sustaining and growth, minimizing cybersecurity and business resilience threats may not be front and center on the minds of these entities. This paper will provide a case study background about a project and effort – the New Jersey Cybersecurity Regional Cluster (NJCRC) - that has contributed significant outreach to New Jersey small businesses to provide free cybersecurity risk assessments to help small businesses prepare …

Emotion Analysis And Topic Modelling Of Supply Chain Discussion During The Covid-19 Pandemic, Suhong Li, Fang Chen, Thomas Ngniatedema

Information Systems and Analytics Department Faculty Journal Articles

This study aims to investigate the supply chain discussion during the COVID-19 pandemic using the supply chain tweets collected between March 2020 and May 2022 globally. The findings reveal an evolving sentiment trajectory: while the users’ sentiment remained neutral in 2020 and 2021, a negative sentiment surged starting in January 2022. Moreover, an emotion analysis indicates a mix of sadness and optimism among Twitter users, with anger gradually intensifying from June 2021 onward. Furthermore, topic modeling reveals distinct themes discussed each year. In 2020, major topics centered around the government’s response to COVID-19, food and medical supply chain crises. By …

Sentiment-Driven Decision Support Systems: A Word Embedding Approach To Analyzing Ceo Earnings Call Transcripts And Stock Market Reactions, Harsha Sammangi, Aditya Jagatha, Hari Gopal Maddireddy

Research & Publications

This study presents a sentiment-driven Decision Support System (DSS) that leverages advanced word embedding techniques—Word2Vec, GloVe, and BERT—to analyze CEO earnings call transcripts and predict stock market reactions. Tra- ditional lexicon-based sentiment models fail to capture the nuanced, contextual language used by executives. By employing pre- trained embeddings and machine learning classifiers, the study enhances the accuracy of sentiment classification. The proposed system integrates quantitative sentiment scores with event study method- ology to assess the impact of CEO tone on stock performance. Thematic analysis further enriches interpretability by identifying recurring patterns in executive com- munication. Results demonstrate that positive CEO …

Uta Datathon 2025 Challenges (Archive), Rubab Shahzad, Srinivasa Sai Abhijit Challapalli, Ghanbarian Behzad, Mei Yang, Clivin Geju, Zecil Yogeshkumar Jain, Jay Mahavirkumar Shah, Sai Nikhitha Chandana

2025 Datathon Challenges-Archive

Timed and Model Challenges from the UTA Datathon 2025

Data-Driven Default Prediction: Insights From Lending Club, Shumaila Gilani, Viktoria Kleer Kliimand

SPARK Symposium Presentations

Peer-to-peer (P2P) lending has transformed consumer credit markets by providing an alternative to traditional banking institutions. LendingClub, a pioneer in this space, facilitates lending between individual investors and borrowers through a data-driven risk assessment model. Our research aims to enhance loan default prediction by developing a more precise classification model based on LendingClub’s historical loan data, ultimately improving risk assessment for investors.

Utilizing a dataset of approximately 650,000 loans from 2007 to 2015, we construct a predictive model to classify loan default risk. Our approach focuses on key financial indicators, including interest rates, borrower grades, debt-to-income ratio, and delinquency history, …

Library Post Partnerships: A Solution To Increase Access To Public Library Facilities In The Las Vegas Metropolitan Area, Wendy Rein

Calvert Undergraduate Research Awards

Resource deserts are areas with residential populations that lack access to public amenities. Current standards in the field of Library and Information Sciences recommend a two-mile buffer zone around public libraries in order to ensure access and use of facilities. In the Las Vegas metropolitan area, multiple library deserts can be identified overlapping with areas designated for community development. This research shows that public libraries provide resources that can improve community metrics, notably educational and vocational outcomes, but that many neighborhoods in the region lack access to a facility. Increasing the distribution of public libraries as they are currently architecturally …

Digital Transformation And The Future Of Work: Closing The Digital Skills Gap, Siu Loon Hoe

Research Collection School Of Computing and Information Systems

The purpose of this article is to discuss the near future digital technology landscape and propose several specific in-demand digital skills for organizations and individuals in the next few years. This article reviews some recent publications from representative inter-governmental, governmental, non-governmental, and commercial organizations on the rise of digital technologies and corresponding growth in digital jobs. Within this context, several specific in-demand skills are proposed by the author who has written a book on the topic of digital transformation. Rapid advancements in digital technologies continue to shape organizational practices and the future of work. To take advantage of emerging digital …

What Does Chatgpt Know About Information Systems?, Daniel O'Leary, Veda C. Storey, Aaron M. French, Joseph R. Buckman, Cecil Chua, Andrew William Green, Grace Gu, Fred Niederman, Francis Pereira, Gary Templeton, Linda Wallace

Faculty Articles

Large language models such as ChatGPT provide efficient access to a wealth of information. However, there are significant questions regarding the depth and quality of knowledge in any one domain. This paper focuses specifically on the information systems (IS) field and assesses ChatGPT’s knowledge. To analyze the extent and quality of information systems knowledge derived from queries to ChatGPT, we used over 3,000 queries from a broad range of exam and quiz questions. These queries were obtained from university courses and professional information system certification exams. The query topics are based on a framework for information systems education, with queries …

Curriculum Preferences And Engagement Of Online Entrepreneurship Students: The Influence Of Age And Gender, S. Andrew Starbird, Jill M. Martin, Trish A. Kalbas-Schmidt

Information Systems and Analytics

It is important for instructors and institutions to create learning experiences that are engaging, effective, and meaningful for students. To achieve these goals, instructors must understand the preferences and interests of their students, build engaging lessons based on those interests, and mitigate content that might make students feel excluded. In-person learning allows instructors to gather information about interests and engagement through direct interaction with students. Gathering information about student interests and engagement is more difficult for asynchronous, self-paced, online training programs. In this paper, we assess the interests, engagement, and disengagement of learners accessing online content focused on entrepreneurship. We …

A Set Of Axioms Providing A Basis For Understanding And Analyzing Work Systems In Organizational Settings, Steven Alter

Business Analytics and Information Systems

This paper proposes 24 axioms that form a basis for understanding and analyzing sociotechnical and totally automated work systems in organizational settings. The introduction identifies reasons for trying to develop axioms of that type. Two background sections compare axioms with other types of “knowledge objects”, illustrate the minimal presence of axioms in the IS discipline, define the domain of relevance for the axioms discussed here, and explain the process of developing those axioms. The axioms are organized in five categories: 1) system in context, 2) system operation, 3) system goals and goal attainment, 4) system uncertainties, and 5) system-related change. …

Can An Llm Use Work System Axioms When Describing Work Systems For Requirements Analysis?, Steven Alter

Business Analytics and Information Systems

This research-in-progress paper presents part of an ongoing project related to using LLMs for describing, analyzing, and designing work systems (including information systems). General axioms that apply to any non-trivial WS or IS might provide a path toward new tools and methods. This paper identifies 24 work system axioms that extend earlier research. They are organized in five categories: 1) system in context, 2) system operation, 3) system goals and goal attainment, 4) system uncertainties, and 5) system-related change. The axioms potentially address the challenge of helping business and IS/IT professionals understand and collaborate around systems in organization. This preliminary …

Collecting Financial Data From Online Sources: Enhancing Large Language Models With Real-Time Search, Yang Li

Department of Information Management and Business Analytics Faculty Scholarship and Creative Works

Timely and accurate access to financial data is crucial for empirical research in accounting and finance. However, current data collection processes are often manual, inconsistent, and difficult to scale. This study asks: How can large language models (LLMs) be effectively used to automate financial data collection? Using design science research methodology (DSRM), the author develops a modular architecture that integrates a real-time search API and auxiliary information processing into LLM workflows. The study applies the model to two tasks: extracting ESG report release dates and identifying customer firm tickers from COMPUSTAT. The system achieves 96% and 95% accuracy, respectively, comparable …