Management Information Systems Commons^™

Early Stem Impressions, Student Engagement, And Readiness For Digitalization, Myron Sheu

Journal of International Technology and Information Management

This study examines how early impressions of science, technology, engineering, and mathematics (STEM) shape business students’ learning behaviors and, ultimately, their readiness for organizational digitalization. Focusing on gender differences, subgroup identities, and perceived obstacles, the analysis uses survey data processed through correlation matrices, regression models, and subgroup heatmaps to trace the relationship between initial attitudes toward STEM and subsequent engagement patterns. The findings reveal consistent links between positive early impressions and active participation in structured STEM activities, along with gender-based distinctions in action preferences. Subgroup analyses further uncover nuanced patterns where stereotypes or perceived barriers correspond with reduced engagement. Collectively, …

The Crowdfunding Paradox In Crisis: Rising Funder Demand Vs. Declining Entrepreneur Supply, Dan Liu, Guangzhi Shang, Cynthia Fan Yang

Journal of International Technology and Information Management

This study investigates how the crowdfunding marketplace responds to major crises, focusing on behavioral shifts among funders and entrepreneurs. Results show a dual impact on platform dynamics. On the demand side, funders become more engaged, with notable increases in the number of backers, average contributions, and total pledge amounts. This heightened activity suggests stronger altruistic motivations, as individuals view crowdfunding as a way to support others during difficult times. On the supply side, however, entrepreneurs act more cautiously, leading to a decline in new project launches. This drop likely reflects increased risk aversion and uncertainty as creators navigate volatile conditions. …

The Influence Of Ai Tool Dependence On Students’ Perceived Preparedness And Confidence In Higher Education, Julia Fichera

Honors Theses and Capstones

As AI tools are becoming more integrated in higher education, how these tools may impact students’ perceptions of their own independent writing and communication skills has been questioned. This study seeks to understand how reliance on AI writing tools in higher education influences students’ perceived preparedness and confidence in their independent writing and communication skills. Survey data was collected from 102 university students; after removing incomplete entries, 99 responses were included in the final analysis. The data was analyzed using descriptive statistics, Spearman correlations, and Mann-Whitney U tests.

The tests reveal that greater AI reliance is associated with lower self-perception …

Influencer Marketing Through The Decades: Evolution And Future Pathways, Raisha Yesmin, Chandan Goswami

Markets, Globalization & Development Review

Influencer marketing has evolved over the past few years, shifting from the conventional way of celebrity endorsement to a data-driven digital world. A systematic review has outlined the history of influencer marketing from the early days of the use of traditional mass media to development of social media platforms, the development of micro and nano Influencer marketing, and the different perceptions about authenticity and trust in consumer.

This study focuses on 37 peer-reviewed articles and books published between 1956 and 2025 from databases such as Scopus, Web of Science, Google Scholar, Emerald Insight, ScienceDirect and Taylor & Francis. Studies shows …

Bridging Mission And Execution: Integrating Participatory Design In Early-Phase Mission Engineering For Stakeholder Alignment And Mission Clarity, Rafi Soule

The Knowledge and Creativity Expo

This research examines mission framing during the early phase of Mission Engineering. Stakeholder interpretations diverge under ambiguity. Interoperability constraints are often not surfaced early. These conditions reduce mission clarity and weaken mission-to-system mapping readiness. The study integrates a participatory design-inspired, artifact-first workflow with RAG-enabled retrieval from a closed corpus to support evidence-grounded reasoning and traceable citations.

Phase 1 uses an online survey to establish baseline patterns in practice (N = 86). Shared understanding is positively associated with mission clarity (r = 0.60, p < 0.001). Phase 2 uses a time-bounded comparative workshop with two conditions. Expert reviewers rate mission statement quality higher for the participatory design condition (mean 3.5) than the traditional condition (mean 2.8). Technical feasibility ratings are similar across conditions. Phase 3 demonstrates RAG-enabled, closed-corpus, retrieval-supported traceability using the Referencer tool. It is reported as a proof-of-concept for evidence-grounded rationale and auditability, and as a pathway …

Two Studies Exploring Signal-Environment Fit In Ai-Assisted Managerial Decision-Making Under Uncertainty, Oleksandr Kondrachuk

Graduate Studies Theses and Dissertations 2026

This dissertation examines how the fit between the characteristics of artificial intelligence systems and the uncertainty environment shapes managerial reliance on AI advice. Organizations increasingly deploy different AI systems, such as predictive and generative, to support management accounting decisions. However, the accounting literature has mainly treated AI as a single undifferentiated entity. This dissertation addresses this gap through two related studies. In the first study, drawing on signaling theory and ecological rationality, I develop a signal-environment fit framework proposing that the reliance on a certain AI system depends on the match between system characteristics (input, process, output) and uncertainty characteristics. …

From Ai Ethics To Ethical Misalignment: Understanding Emerging Risks In Agentic Ai Systems, Deb Tech, Aravindh Sekar

Research & Publications

The emergence of Agentic AI systems is reshaping organizational processes through continuous adaptation, autonomous coordination, and real-time operational execution. Unlike traditional artificial intelligence applications focused on discrete decision-making tasks, Agentic AI systems operate continuously and interact dynamically with organizational environments. Existing AI ethics literature has primarily emphasized issues such as fairness, bias, transparency, and accountability within static or decision-centric AI contexts. Similarly, traditional AI alignment research has largely focused on ensuring that AI systems pursue intended goals and remain controllable under specified operational conditions. However, these approaches may be insufficient for explaining how ethical risks evolve with continuously operating autonomous …

Cultivating Workplace Happiness In Technology Workers, Cherie A. Herrin

Graduate Studies Theses and Dissertations 2026

Technology workers are central to the innovation and performance of modern organizations and retaining them is a persistent challenge for modern organizations. One important aspect of retention in many contexts is Workplace Happiness, which has been argued to improve worker performance and reduce turnover; however, there is little empirical evidence of the effectiveness of these interventions for technology workers. This dissertation develops a model in which two affective states, Workplace Happiness and Workplace Distress, mediate the relationships between six socio-technical work conditions and two behavioral outcomes: Turnover Intent and Work Execution (i.e., self-rated work performance and quality). A systematic literature …

Developing A System Dynamics Framework For Sustained Digital Transformation, Quintal Battle

Graduate Studies Theses and Dissertations 2026

Despite significant organizational investments, more than 70% (Ellmer et al., 2025) of digital transformation initiatives fail. The existing literature evaluates transformation mechanisms as separate factors rather than as interacting, evolving feedback systems. This dissertation addresses this gap by developing and validating a system dynamics framework for sustained digital transformation. Guided by three research questions, the study examined the mechanisms that enable or constrain transformation, identified mechanisms distinctive to digital transformation contexts, and evaluated how existing change management frameworks guide transformation efforts. The study followed a six-phase methodology that combined a systematic literature review of peer-reviewed studies, empirical cases, causal loop …

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.

Enhancing Hospital Length Of Stay Using Ai Agent Modular Capabilities, Dami Fred

Theses and Dissertations

Hospital Length of Stay (LOS) analysis is essential for managing hospital efficiency and financial performance, yet the analytical depth required remains inaccessible to many organizations due to technical and resource constraints. This thesis introduces the AI Agent Modular Capabilities (AIAM) framework, which decomposes LOS analysis into discrete, codifiable capabilities integrated into a single skill activatable with one prompt. Seven AI Agent Modular Capabilities were developed using a Work Breakdown Structure and tested against a simulated hospital scenario across three successive skill versions. Results demonstrate that the packaged AIAM skill produces comprehensive, consistent, multi-section LOS analyses from a single interaction, outperforming …

Advancing Cybersecurity Practice: Explainable Machine Learning For Network Intrusion Detection, Adam Grabowski, Shengjie Xu

Journal of Cybersecurity Education, Research and Practice

This research investigates explainable artificial intelligence (XAI) integration within machine learning (ML)-based intrusion detection systems (IDS), focusing on distinguishing malicious from benign network activities. We employed Random Forest and XGBoost models evaluated on widely recognized datasets, including NSL-KDD and UNSW-NB15, using both binary and multi-class classification tasks. The objective was to enhance cybersecurity operations through improved model transparency and interpretability. By integrating SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations), the study offers comprehensive global and local insights into model decision-making processes. Results demonstrate SHAP's effectiveness in providing a broad, dataset-wide understanding of feature interactions and importance, while …

Using It And Accounting Education Pedagogy To Address Smaller Firms' Cybersecurity Concerns, Darlene A. Adkins, William Lyle, Walter Smith

The North American Accounting Studies

This study begins by exploring the cybersecurity activities and practices of small public accounting firms. It also examined how a cross-disciplinary curriculum design could be useful to accounting programs that are challenged to integrate cybersecurity-related content while preserving the core accounting knowledge valued by smaller accounting firms. Through interviews with current practitioners from small and regional firms, the research investigated the extent to which cybersecurity concerns impact their practice, how cybersecurity activities are staffed, and the perceptions of current practitioners on the state of accounting education related to information technology (IT) and cybersecurity. The participants expressed a desire for accounting …

Forecasting Aviation Carbon Emissions With Tree-Based Machine Learning: A Case Study Of Turkish Airlines Operational Data, Demet Dağlı Phd, Ednan Ayvaz Phd

International Journal of Aviation, Aeronautics, and Aerospace

The global aviation industry plays a critical role in economic development and international connectivity. However, it also contributes significantly to environmental challenges, particularly through fuel consumption and carbon emissions. The aviation sector is responsible for approximately 1–2% of global CO₂ emissions, and its environmental impact is expected to increase substantially by 2050. This study proposes a machine learning (ML)-based decision support framework to forecast carbon emissions in the airline sector. Using Turkish Airlines’ quarterly operational data from 2008 to 2023, Decision Trees, Random Forest, XGBoost, and Extra Trees models were applied to predict carbon emissions. Key variables include revenue passenger …

Exploring The Impact Of Negative Ewom On Hotel Booking Intention Through The Lens Of Attribution Theory And The Moderating Role Of Recovery Justice, Antonio Jose Ogaz-Muñoz

Electronic Theses and Dissertations 2020 - Present

Negative electronic word-of-mouth (EWOM) has emerged as a critical factor influencing customer behavior, particularly in the hospitality industry. Despite extensive research grounded in failure attributions and service recovery theories, few studies examine the combined impact of failure attribution and recovery justice on hotel booking intentions. Using a 2x2x2x2 between-subjects experimental design with 373 responses, this research investigates how customers' perceptions of failure attribution influence their booking intention after exposure to negative EWOM, and if perceived recovery justice can mitigate the negative effects of these attributions. Results show that booking intention declined most when reviews framed the failure as hotel-caused and …

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 …

Towards Resilient Data Governance: An Industry Study On Practitioner Perspectives On The Current State Of Data Governance, Anthony Mazzarella Iii

Theses and Dissertations

Experts agree that data governance is crucial for managing data as an asset, yet in practice, it remains inconsistently defined, unevenly implemented, and poorly understood. Earlier industry studies offered valuable descriptive benchmarks of governance adoption and professional roles, but provided limited theoretical insight and little empirical evidence explaining how governance functions within organizations. Building on this foundation, the current research offers a more substantial and analytically rigorous contribution by transforming descriptive observations into empirical explanations. Through a collaborative academic–industry partnership, the study utilizes a global survey of 565 respondents (348 valid cases) to analyze governance structures, motivations, challenges, and the …

A Study Of Configuration Management Database (Cmdb) Adoption In It Service Management (Itsm) Implementations Within Nj Community Colleges, Fredrick Dande

All-Inclusive List of Electronic Theses and Dissertations

This study examines the adoption of Configuration Management Databases (CMDBs) in IT Service Management (ITSM) implementations within New Jersey (NJ) community colleges. Despite the well-documented benefits of CMDBs—such as faster issue resolution, improved compliance, and greater visibility across IT infrastructures—implementation success rates remain low. As technology continues to enhance production capabilities and expand access to information, the need for centralized configuration visibility has become critical. A CMDB provides a single system of record for IT assets and services, helping organizations manage outages, assess changes, maintain compliance, and improve asset tracking. This research used an online survey to collect data from …

Feedback Strategies In The Market With Uncertainties, Mustapha Nyenye Issah

Graduate Theses and Dissertations (2019 - present)

This paper explores how established firms use strategic advertising to deter new competitors in uncertain markets. Specifically, it models a situation where market demand evolves unpredictably - captured by the CKLS stochastic process, and the incumbent firm may be either strong or weak, a fact hidden from potential entrants. For a company already in the market, advertising is not just about driving immediate sales, it is a strategic tool to project an image of strength and deter potential new competitors. On the other side, a business thinking about entering that market faces a high-stakes, irreversible decision. It will typically hold …

An Active Learning Simulation For Teaching Hadoop In An Undergraduate Business Curriculum, Ruben A. Mendoza Ph.D., Liyuan Liu Ph.D.

International Journal for Business Education

This paper shows the effectiveness of an experiential learning activity for teaching core Hadoop concepts to undergraduate business students across all majors (Finance, Marketing, etc.). The complexity of Hadoop, with its distributed computing model and deep technical requirements, presents significant challenges in educational settings. The simulation was developed for an introductory course in a Business Intelligence & Analytics (BI&A) curriculum and adapted for use in a Database Management (DB) course. Results show significantly higher performance on questions centered on the topic in an online test preparation game with a total of 236 students in 11 different offerings of the course. …