Management Information Systems Commons^™

People Orientation And Thing Orientation In Business Majors: Implications For Assessing The Impacts Of Cross-Functional Business Program Curricula, Todd J. Hostager, David A. Christopher, Kristy J. Lauver, Christopher Knowles

International Journal for Business Education

Background/Introduction/Purpose:  Prior research documented significant differences in people orientation and thing orientation (PTO) based on type of major and sex.  This study examines a set of measures for helping business programs to assess whether they are producing graduates equipped to consider both people and things when making decisions, regardless of their major or sex.

Methods/Design:  Students in a strategic management capstone course spent a single 75-minute session responding to a brief new venture pitch by identifying what types of additional information they would need to decide whether to invest in the business.  Three Likert-scaled options gauged the extent to participant …

How Does An Advanced Educator Utilize Digital Language Teaching And Learning By Integrating Linguistic, Cultural, And Business Life Perspectives?, Hely Westerholm, Pirjo Takanen-Körperich, Ph.D.

International Journal for Business Education

This paper explores advanced educators’ digital working capabilities in online language teaching, with a particular focus on sharing Finnish language and culture in a foreign context. Cultural and linguistic knowledge play a significant role in enhancing students’ motivation to study foreign languages. In today’s educational environment, digitalisation and globalisation exert an increasing influence on foreign language teaching and learning.

The digital learning environment has brought about a paradigm shift in foreign language didactics. Digital language learning is both challenging and holistic in nature. Continued foreign language learning is simultaneously an individual and a social process, as digital learning cultures impose …

How Grades Changed Before, During And After Covid-19 In A Business College, Douglas R. Moodie, Alison Keefe, Robin Cheramie

International Journal for Business Education

Purpose: This study examines how undergraduate business student grades changed before, during, and after the COVID-19 pandemic using a large institutional dataset from a business college in a US university between 2015 and 2024.

Method: The analysis includes over 390,000 student-course observations and incorporates instructional modality, student demographics, course characteristics, and prior academic performance.

Results: Results show a substantial increase in mean course grades during the COVID-19 period, followed by a partial reversion post-pandemic to levels that remain above pre-pandemic trends. These patterns are consistent across modalities, demographics, and departments.

Implications: The findings raise important questions regarding the interpretation of …

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%, …

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 …

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 …

Examining Digital Transformation In Maritime Logistics: A Big Data Perspective, Jiyoon An

Atlantic Marketing Association Proceedings

No abstract provided.

Ai-Scm Cmm: A Capability Maturity Model For Artificial Intelligence Integration In Supply Chain Management, Lordt Becklines, Omar F. El-Gayar, Patti Brooks, Insu Park

Annual Research Symposium

Artificial intelligence is increasingly deployed in supply chain management, yet many organizations struggle to align adoption efforts with process readiness, data quality, governance, and workforce capabilities, and they still lack validated supply chain specific roadmap for assessing readiness, sequencing investments, and reducing implementation risk. This study develops and evaluates a Capability Maturity Model for Artificial Intelligence Integration in Supply Chain Management to address that gap. Using a design science research approach, the study synthesizes prior literature and practitioner knowledge to define maturity dimensions, capability indicators, and staged progression levels for AI integration in supply chain contexts. The artifact and assessment …

A Capability Maturity Model For Artificial Intelligence Integration In Supply Chain Management, Lordt Becklines

Dissertations

Artificial Intelligence (AI) is transforming Supply Chain Management (SCM), yet many organizations struggle to assess their readiness for AI adoption and to understand how AI capabilities develop across maturity stages. This dissertation addresses this gap by developing a Capability Maturity Model (CMM) for AI integration in SCM, grounded in Organizational Information Processing Theory (OIPT), the Resource-Based View, and related capability frameworks. The model provides a structured approach for evaluating an organization's information-processing requirements, resource configurations, and alignment needed for effective AI-enabled supply chain operations.

Using a design science research approach, the AI-SCM CMM and its associated assessment instrument were derived …

Business Process Redesign For Reducing Undelivered Product Return Losses In E-Commerce – An Explainable Ai Approach, Venkataraghavan Krishnaswamy, Deepa R, Himanshu Sharma

Journal of International Technology and Information Management

Product returns in e-commerce affect the profitability of the e-tailer. We adopt a two-stage approach to reduce undelivered product returns in an e-commerce firm. First, we develop and compare machine learning techniques—logistic regression, decision trees, Naïve Bayes, random forest, adaptive boosting, gradient boosting, stochastic gradient boosting, and deep neural networks—on their ability to predict undelivered returns. Next, we use explainable methods, such as relative importance and Shapley values, to develop insights from the best-performing machine learning model. Finally, we use these insights and the predictive model to redesign the firm’s order fulfillment and return processes. A Post-implementation evaluation of the …

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 …

Integrating Social Media And Entrepreneurship: A Study Of Social Media, Creativity, Strategy, And Innovation In Building A Successful Brand Presence In The Digital Era, Lys Ishiari

Undergraduate Honors Theses

Social media has completely changed what it means to start a business. It was once just a place for personal interactions but is currently a powerful channel that drives the growth of multiple industries (Felicia, 2022). Now, anyone can use their personal creativity and hobbies to build a brand, especially on apps like Instagram, YouTube, and TikTok. The purpose of this study is to identify the common themes behind social media entrepreneurs’ success, which allows individuals to become both creators and business owners in the digital age. Furthermore, a key part of writing this paper is personal; it connects to …

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 …

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 …

Investigating The Relationship Between Noun Classes And Plant Folk Taxonomy In Chasu Language Of Kilimanjaro Region In Tanzania, Peter Rabson Mziray

Journal of Humanities and Social Sciences

The current study investigates the relationship between noun classes and plant folk taxonomy in Chasu (G 22). The study focuses on two objectives: the first objective is to describe the plant folk taxonomy in Chasu and the second objective is to determine the relationship between noun classes and plant folk taxonomy in Chasu. Data were collected from rural villages in Same and Mwanga districts by using free listing, field interviews (jungle-walk-and-identify), and written texts containing Chasu plant names. The findings reveal that Chasu folk taxonomy reflects different ethnobotanical categories; including a unique beginner which is mmea/mimea ‘plant(s)’, and three life …

Skill Evolution In The Age Of Ai-Utilizing Text Analytics For Skill Gap Analysis To Prepare Women For Leadership Roles, Blenda G. Mutuma, Marcelline Ouma, Beth Kanyiri

Communications of the IIMA

ABSTRACT
The rapid advancement of Artificial Intelligence (AI) is transforming the global workforce, presenting both opportunities and challenges for leadership development, particularly for women. As AI automates routine tasks and redefines skill requirements, there is a growing demand for uniquely human capabilities such as emotional intelligence, creativity, and strategic thinking, qualities that are inherently strong and often highly associated with women. Research indicates that women typically score higher in emotional intelligence, particularly in areas such as empathy and relationship management, which are critical for effective leadership (Goleman, 2020). Furthermore, studies by McKinsey & Company (2022) highlight that gender-diverse leadership teams, …

Enhancing Sarcasm Detection With Contextual Factors For Improved Model Robustness Based On Bmlrf, Walter O. Yodah, Alice S. Mataruse, Never O. Kangara

Communications of the IIMA

Detecting sarcasm in text remains a critical yet challenging task in natural language processing (NLP). Despite significant advances through deep learning, particularly the use of transformer-based architecture like BERT, sarcasm detection models still face challenges in achieving high accuracy. A major limitation lies in their insufficient incorporation of contextual awareness, including conversational history, social inter- actions, emotional cues, and cultural factors. To address this, this paper proposes the BERT with Meta-Feature Logistic Regression Fusion (BMLRF) model, which in- tegrates sentence-level embeddings from a pre-trained transformer with meta-features capturing social, emotional, and cultural contexts. The model also leverages multi-turn dialogue history …

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 …

Orchestrating Complexity: The Art Of Virtual Leadership In System Modelling, Vijay Kumar Sonawane, Bipllab Roy, Purnendu Bikash Acharjee, Indu Pv

Northeast Journal of Complex Systems (NEJCS)

This paper explores the dynamics of virtual leadership within global remote work environments, focusing on the application of complex system modelling to understand and enhance leadership efficacy. The application of computational modelling has been a regular feature in economics, science and technology fields, however its application in virtual leadership with linkage to sport leadership appears to be a novel concept. Adopting a multidisciplinary approach, this paper incorporates Game Theory as a conceptual framework to make the leadership model more relevant and applicable that can offer simpler understanding of complex play of leadership drivers. The model incorporates five key leadership dimensional …

Crypto Accounting Market Dynamics: Advanced Econometric Analysis Of Earnings Impact With Bert-Powered Genai Models, Karina Kasztelnik, Steven Campbell, Eva K. Jermakowicz

Journal of Global Awareness

This study is, the authors believe, a groundbreaking investigation into the impact of cryptocurrency news on the earnings of publicly traded companies. Using advanced Generative AI (GenAI) models and the BERT framework for sentiment analysis, we integrated comprehensive data from the Financial Modeling Prep API. This enabled us to employ a rigorous event study methodology and advanced machine learning algorithms. Valuable insights were derived from the BERT model, shedding light on the reasons behind abnormal returns and facilitating a thorough analysis of material and immaterial impacts. The study’s findings highlight the significant influence of both positive and negative cryptocurrency news …

Colaboración Interdisciplinaria: Tablero De Control Para Una Institución Politécnica R01 En Los Ee. Uu. [Libro], Cristo Leon Ph.D., Victor Hugo Guzmán Zarate Ph.D.

STEM for Success Showcase

Introducción
En el ámbito académico, la colaboración interdisciplinaria emerge como un imperativo estratégico y un desafío multifacético. Este estudio tiene un enfoque eminentemente práctico y busca diseñar y aplicar una herramienta específica —un tablero de control interactivo— para facilitar y mejorar la colaboración interdisciplinaria en un entorno académico real. La complejidad inherente a este desafío se refleja tanto en el dinamismo del ambiente investigativo como en la falta de definiciones operacionales cohesivas que faciliten la evaluación y visualización de la colaboración interdisciplinaria. Para abordar estos desafíos, el presente estudio se llevó a cabo en la Facultad de Ciencias y Artes …

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 …

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, …

Perceptions Of Artificial Intelligence In Healthcare: A Qualitative Study Among Physicians And Nurses In Florida, Aaron Miri

MUSC Theses and Dissertations

This investigation will leverage participant focus group interviews with 32 clinicians (16 nurses / 16 physicians) to study what, if anything, is inhibiting AI adoption across the hospital. Specific physicians will be sourced across the key service lines of primary care, oncology, cardiology, behavioral health, and emergency department medicine, as these tend to be patient volume driven and thus have the maximum amount of potential for positive impact leveraging AI. Nurses in these same departments will be assessed to analyze if there is a similarity or difference between the nursing and physician AI adoption barriers.

Enhancing Interoperability In Geospatial Analytics Systems: Navigating Diverse Standards For Optimal Data Integrity, Justin W. Bennett

Dissertations

In this study, we explore the domain of geospatial analytics, focusing on how different geographic information systems (GIS) and technology solutions can better communicate and maximize interoperability. Central to our study is the impact of this interoperability on data integrity, which is critical for accurate decision-making in domains like urban planning, environmental management, and national security. Accurate decision-making requires modeling ecosystems that are easy to comprehend and are often intricate due to their physical or artificial nature and the diversity of geospatial parameters; therefore, it is essential to gather a comprehensive array of spatial and temporal measurements. We examine the …

Assessing The Readiness And Awareness Of Key Stakeholders In The Egyptian Maritime Industry To Implement Smart Green Port Practices: A Case Study Of Alexandria Port, Mohamed Elhussieny, Ahmed El Kassar, Kareem Tonbol

Blue Economy

This study explores the readiness and preparedness of the major stakeholders within the Egyptian maritime sector towards the adoption of smart green port practices, with particular consideration given to Alexandria Port. With growing environmental threats, the need to support sustainable maritime operations is high. The study evaluates the current state of the ecosystem at Alexandria Port and benchmark it against international standards to highlight the critical gaps that should be covered for the effective execution of smart green port approaches. The results indicate that, although stakeholders have basic knowledge of sustainability issues, large perceived barriers, particularly regarding resource availability, limited …

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 …

Llm-Guided Multimodal Information Fusion With Hierarchical Spatio-Temporal Graph Network For Sentiment Analysis, Yujie Jin, Yong Wang, Yuzhe Wang, Qiyang Chen, Bin Hu, Yanling Han, Chaoyin Ma, Witold Pedrycz

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

Multimodal sentiment analysis aims to attain a precise comprehension of emotions by integrating complementary textual, visual, and audio information. However, issues such as sentiment discrepancies between modalities, ineffective integration of multi-modal information, and the intricacy of order dependency significantly constrain the models' efficacy. The authors propose an LLM-guided Hierarchical Spatio-Temporal Graph Network (L-HSTGN). By multimodal large model feature enhancement, bidirectional spatio-temporal joint modeling, and dynamic gate fusion mechanism, they effectively address the aforementioned problems. Firstly, they produce cross-modal emotion pseudo-labels based on the multimodal large model, and the single-modal representation was optimized by combining adversarial regularization. Secondly, they develop a …

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 …

Ict Technologies: Origins, Development, And Applications In Selected Examples From The Economy And Society, Tomasz Parys

Studia i Materiały Wydział Zarządzania Uniwersytet Warszawski

Purpose: The primary purpose of this paper is to present information and communication technologies, collectively referred to as ICT, as a determinant of the development and functioning of the modern world through the prism of tools, models, concepts and platforms that enable their application. This is accomplished in the article by presenting selected technologies along with illustrating the scope and possibilities of their use.

Methodology: The scope of the research included ICT technologies, their development and evolution, as well as their application in various fields. Extensive source material was collected, with a primary focus on academic publications, including …