Business Commons^™

Comparing Funders' Altruism Versus Self-Interest: Leveraging The Context Of Crisis, Dan Liu, Guangzhi Shang, Cynthia Fan Yang

Journal of International Technology and Information Management

While reward-based crowdfunding has widespread popularity, the motivations driving funders, balancing self-interest and altruism, have remained puzzling. Prior research has been constrained by examination methods and produced mixed findings regarding the weight of altruism versus self-interest among funders. Our study takes a fresh perspective, delving into funder behavior amid a major crisis—the tumultuous backdrop of the COVID-19 pandemic. Our findings reveal that funders not only display an increased willingness to contribute but also significantly amplify their contributions, particularly to projects in crisis-affected regions, irrespective of external incentives like rewards. This underscores the prevalence of altruistic motives among funders in challenging …

Artificial Intelligence And Environmental Sustainability: Review And Research Directions, Troy Strader, Yu-Hsiang (John) Huang, Yu-Ju Tu

Journal of International Technology and Information Management

Environmental sustainability is one of the most important and complex issues currently facing our global society. One solution to some aspects of this problem could come from artificially intelligent systems and data analytics methods. The objective for this study is to identify the range of recently published research that addresses issues involving the convergence of artificial intelligence (AI) and environmental sustainability. A systematic literature review produced a sample of 62 journal articles from 2018-2024 that were each categorized into one of six research themes that included studies of AI and the ways in which it impacted natural resources, energy and …

Understanding The Determinants Of Blockchain Adoption: An Empirical Study, Amarpreet Kohli, Nihar Kumthekar, Piyush Shah, Rebecca Jauch

Journal of International Technology and Information Management

Blockchain technology (BT) has the potential to enhance security and robustness of transactions through a distributed ledger bookkeeping process. This study employs technology-organization-environment (TOE) framework and threat-rigidity theory (TRT) to examine whether perceived disruption caused by COVID-19 pandemic significantly impacted the adoption of BT, and inclination to adopt BT in the US. The COVID-19 pandemic provided a unique backdrop, as it affected businesses across all industries, sizes, and geographies. Results show a non-significant effect of perceived pandemic disruption on the current stage of BT adoption and intention to adopt BT. However, disruption readiness positively influences the current stage of BT …

Comparing Vr And Tv In Nigeria And The U.S.: Impacts On Empathy, Engagement, And Enjoyment, Jinhee Yoo, Eugene A. Ohu, Radosław Mącik

Journal of International Technology and Information Management

This study (N = 170), involving participants from Nigeria and the U.S., investigated how different technologies (TV and VR) affect users' empathy (α = .93), engagement (α = .93), enjoyment (α = .93), preferences, and likelihood of technology use. Participants watched an animated documentary titled “Is Anna OK?” at two different time points, utilizing VR (Oculus Rift S) and TV, following which they completed measuring empathy, engagement, enjoyment, device preference, and usage likelihood. Analysis via one-way ANOVA and chi-square tests revealed that VR users reported significantly higher empathy and enjoyment compared to TV viewers, particularly on second viewing. Combining both …

Predicting Crises On The African Frontier Stock Markets With Investor Sentiment Indicators: A Machine Learning Approach, David Korsah, Lord Mensah

Journal of International Technology and Information Management

This study examined the predictive ability of machine learning algorithms in identifying crises within African stock markets. The study employed seven distinct machine-learning models, analyzing historical stock prices from eight stock markets, three major sentiment indicators, and the exchange rates of local currencies against the US dollar, with each data spanning from May 1, 2007, to April 1, 2023. Extreme Gradient Boosting (XGBoost) emerged as the most effective algorithm for predicting crises. Historical stock prices and exchange rates were identified as the most critical features for prediction. On the sentiment side, investors’ perceptions of potential volatility on the S&P 500, …

A Conceptual View Of Data For Decision-Oriented Databases: A Knowledge-Driven Approach, Sung-Kwan Kim, Wenjun Wang, Seunghyun Kim

Journal of International Technology and Information Management

Typical database design goes through three levels of data modeling: conceptual modeling, logical modeling, and physical modeling. In particular, conceptual modeling is important since it captures and documents user data requirements. Conceptual modeling serves as a blueprint for designing a database by defining information content to be included in a database. Presently, decision-oriented databases have no well-accepted conceptual modeling approach to apply. While some use conceptual modeling approaches for transaction-oriented databases such as the ER (Entity-Relationship) model, they are not well-suited for decision-oriented databases. It is hard to map from the ER Model to decision-oriented data models. Others attempt to …

Preventive Health Care Information Seeking Behaviors Among Baby Boomers In Taiwan, Alexander N. Chen, Michael J. Rubach, Tracy Suter, Hsin Ke Lu, Mark E. Mcmurtrey

Journal of International Technology and Information Management

Preventive health care is widely acknowledged as one of the most effective ways to reduce medical costs and enhance people's health. Preventive health care information (PHCI) is a crucial component. This study examines the PHCI-seeking behaviors of Taiwanese baby boomers. The study found some support for the idea that the preferred media used influenced the likelihood of Information seeking behavior. People with good health conditions were found to be more likely to seek PHCI, while people with greater health care needs sought out PHCI less frequently. The study examined social influences, which were found to be important. Three different types …

Pedagogy In The Age Of Ai: Exploring Generative Ai For Higher Education, Alison Munsch Phd

Journal of International Technology and Information Management

Generative Artificial Intelligence (AI) presents transformative opportunities for higher education, enabling personalized learning, enhanced student engagement, and efficient pedagogical practices. This tutorial-style article guides educators in integrating generative AI into their classrooms through hands-on activities, practical strategies, and reflective exercises. It explores the capabilities of AI tools such as ChatGPT, their applications across disciplines, and the ethical considerations for their use. By cultivating critical thinking and fostering student readiness for AI-driven futures, this article underscores the transformative potential of generative AI in higher education with an emphasis on the academic areas of business analytics, information systems, and computer science.

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 …

Predicting Global Healthcare Supply Chain Delays: A Machine Learning Approach Leveraging Country-Level Logistics Metrics, Jeevan Sai Gali, Nima Molavi, Sepideh Alavi

Journal of International Technology and Information Management

In global healthcare logistics, ensuring the timely delivery of medical commodities is critical, particularly in low- and middle-income countries characterized by infrastructural limitations and operational uncertainties. This research introduces an advanced, data-driven predictive framework designed to forecast delivery delays by synthesizing granular, internal shipment-level data from the USAID Global Health Supply Chain Program (GHSC-PSM) with external country-level logistics capabilities indicators derived from the World Bank’s Logistics Performance Index (LPI). Rather than relying on retrospective trend analyses, this study employs machine learning algorithms such as Random Forest, XGBoost, Support Vector Machines (SVM), and Multi-Layer Perceptron (MLP) to detect …

Modeling Healthcare Managerial And It Sophistication, Gary Hackbarth, Teuta Cata

Journal of International Technology and Information Management

Sophisticated healthcare managers understand that managing information and selecting technology are two related but separate skill sets. Information Technology Governance (ITG) allows healthcare organizations to deal with complex issues, compete effectively, and support patient needs by integrating managerial and IT sophistication to improve business performance. An organizational model that merges IT and managerial sophistication by improving ITG to forecast business outcomes must grasp the intricacies of technology to acquire, manage, and utilize information technology in alignment with business strategies. The proposed model differentiates between internal and external IT management strategic thinking, emphasizing the critical need to govern IT effectively by …

Securing Mobile Payments: The Impact Of Blockchain Technology On Transaction Integrity, Snehal Chaudhari

Electronic Theses, Projects, and Dissertations

This study investigates how blockchain, tokenization, and smart contracts improve security and transparency in mobile payment systems, addressing critical vulnerabilities in the increasing mobile payments industry. The main research questions are: (RO1) How can integrating blockchain technology improve the transparency and security of mobile payments? (RO2) Why do tokenization and smart contracts play a crucial role in improving security for mobile payments?

Case study outcomes include: (RO1) Shido Wallet's decentralized blockchain provides more control and transparency over their financial transactions, overcoming traditional security and trust concerns. Project Khokha illustrates blockchain's scalability and efficiency by allowing for high-speed, confidential interbank transactions. …

An Analysis Of Security Risks Posed By Text-Based Generative Ai And Corporate Security Weaknesses Leading To Data Leaks, Tashya Rakshana Byreddy

Electronic Theses, Projects, and Dissertations

ABSTRACT

Generative AI (GenAI) has become a fundamental part of modern life, influencing how we work, learn, and interact with technology. This project focuses specifically on text-based GenAI, which is widely used for tasks such as information gathering, code improvement, and content creation. Despite its benefits, it presents significant security risks that are often underestimated by users. This project investigates these risks and the corporate security gaps that lead to unintentional data leaks. The project also provides a brief overview of Large Language Models (LLMs), which are based on the deep learning technique known as Transformer architecture, used for performing …

Suspicious Activity Recognition Using Computer Vision, Chaitanya Krishna Karanam

Electronic Theses, Projects, and Dissertations

This culminating experience project explored innovative methods for enhancing anomaly detection in video surveillance systems, a vital concern for public safety and security management. The research questions addressed were: Q1) What emergent approaches in Activity-based Human Action Recognition (AbHAR) lead to significant advancements in surveillance technology driven by human cognition? Q2) How can the processing and detection phases of video monitoring systems be optimized for improved efficiency? Q3) What are the advantages of ensemble approaches over individual algorithms in enhancing the robustness and accuracy of anomaly detection systems in video surveillance?

The findings were: (Q1), That deep learning methodologies, particularly …

The Practical Adoption And Application Of Blockchain Technology Within The Beverage Industry, Alexander Adams Jr

Electronic Theses, Projects, and Dissertations

Abstract

The beverage industry is facing heightened scrutiny as the demand for transparency and accountability reaches new heights. In the age of information technology, companies must prioritize enhanced traceability to ensure product safety, comply with government regulations, maintain customer trust, and protect brand integrity. This thesis explores the potential of blockchain technology as a solution to these challenges, focusing on its ability to decentralize data, improve traceability, and expedite response times during safety recalls. The research provides an overview of the evolution of food safety regulations, beginning with the first establishment by Upland Sinclair, and examines current traceability practices and …

Identification Of Distressed Real Estate Properties Using Natural Language Processing/Machine Learning, Pavithra Sirigiri

Electronic Theses, Projects, and Dissertations

The Real Estate industry is a great asset class that involves constructing, buying, and selling property. Although technology has made significant progress in buying and selling real estate properties online, via Zillow and Redfin, the ways to find distressed real estate properties as an investment opportunity seem to be lacking. This culminating experience project explored how to use machine learning to classify a property as distressed or non-distressed. The research questions are: Q1. How can Natural Language Processing methods like Latent Dirichlet Allocation (LDA) be leveraged to identify distressed and non-distressed real estate properties? (Tijare & Rani, 2020) and Q2. …

Mitigating Credit Card Fraud In European Countries, Gurninder Singh

Electronic Theses, Projects, and Dissertations

The detection of credit card fraud is essential to lower monetary losses and boost consumer trust in financial institutions. The effectiveness of two machine learning models, the Hidden Markov Model (HMM) and Logistic Regression, in identifying credit card fraud was examined in this study. Data was gathered from a large dataset of transaction records to analyze each model's projected accuracy, precision, and recall. The research questions addressed are: (Q1) In terms of recognizing credit card fraud, how do artificial neural networks and decision trees perform differently? (Q2) What are the comparative accuracy levels of Markov versus Logistic Regression when it …

The Impact Of Benevolent Sexism On Evaluations Of Female Leaders, Daniella A. Lockhart

Electronic Theses, Projects, and Dissertations

This study investigated the impact of benevolent sexism beliefs on evaluations of female leadership candidates in hiring decisions, as well as the moderating effects of occupational gender composition and the gender of the comparison candidate. While seemingly positive, benevolent sexism subtly reinforces traditional gender roles by portraying women as warm but less competent, particularly in leadership roles where competence is crucial. The data in this study was collected from 146 participants via an online questionnaire using MTurk and snowball sampling. Aligning with previous research on gender bias, results indicated that benevolent sexism negatively impacts perceptions of competence and warmth, especially …

Comparative Assessment Of Machine Learning And Deep Learning Models For Drug Effectiveness Using Sentiment Analysis, Blessing Ogechukwu Nwogu

Electronic Theses, Projects, and Dissertations

In recent years, the proliferation of online patient-generated drug reviews has created a valuable resource for assessing drug effectiveness and patient satisfaction, with sentiment analysis emerging as a powerful tool for extracting insights from this unstructured data.

This culminating research project conducted a comparative analysis of traditional Machine Learning (ML) and Deep Learning (DL) models for assessing drug effectiveness using sentiment analysis of participant reviews. The research aimed to evaluate the performance of Support Vector Machine (SVM), XGBoost, Random Forest, Long Short-Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT) models in this context. This culminating research project addressed …

Digital Footprint Management: Youth And Social Media On Protecting Your Online Identity And Reputation, Jorge Aguiar

Electronic Theses, Projects, and Dissertations

Fifty percent (50%) of youth between the ages of 8 and 17 have been discovered to use the internet and social media services on a daily basis. This (50%) also demonstrates the rising issues between digital footprints, cyberbullying, and cybercrime rates against these types of youth. It was discovered that youths, between the ages of 8 and 17 years old, are savvy with technology, based on gender, grade, and socioeconomic status, but also lack the awareness of their own digital footprint and how to manage it. Researchers have discovered that issuing cybersecurity awareness school lesson plans and parental controls may …

Leveraging Business Intelligence Tools For Enhancing Career Services In Higher Education, Vedanti Aghaw

Electronic Theses, Projects, and Dissertations

In today’s competitive job market, career services in higher education are crucial for equipping students with the skills and readiness they need to succeed professionally. Yet, traditional career service models often fall short of addressing the evolving needs of students due to limited abilities in tracking engagement, evaluating program effectiveness, and predicting job market trends.

This study addresses three key research questions: Q1. How can BI tools improve data-driven decision-making within career services? Q2. What role does predictive analytics play in enhancing career success forecasting? Q3. How can BI tools assist in optimizing resource allocation within career centers?

The findings …

Using Ai Tools To Unmask Sarcasm, Sidra Tehniyath Lnu

Electronic Theses, Projects, and Dissertations

Sarcasm can be identified in newspaper headlines in digital communication, as it is contextual and has low inter- and intra-observer reliability. This research aims to improve sarcastic comment identification using a natural language processing approach, especially the BI-LSTM. The primary concern is to use fine-tuning methods to enhance the ways that strengthen the sarcasm identification rate, which regards the factors that complicate an automatic identification process. The study explores the impact of techniques such as early stopping, optimal loss function selection, and hyperparameter tuning to enhance the model's performance. The research questions are: Q1) How can fine-tuning techniques for BI-LSTM …

How Can Researchers Be Influenced To Comply With Guidelines Of Research Data Management?, Rowena Van Houwelingen, Guido Ongena, Pascal Ravesteyn

Communications of the IIMA

How come Open Science is a well-shared vision among research communities, while the prerequisite practice of research data management (RDM) is lagging? This research sheds light on RDM adoption in the Dutch context of universities of applied sciences, by studying influencing technological, organizational, and environmental factors using the TOE-framework. A survey was sent out to researchers of universities of applied sciences in the Netherlands. The analyses thereof showed no significant relation between the influencing factors and the intention to comply with the RDM guidelines (p-value of ≤ .10 and a 90% confidence level). Results did show a significant influence of …

Risk Identification And Mitigation In Agile Software Re-Engineering: A Case Study, Chiara Fasching, Peggy Gregory, Nicholas Mitchell, Charlie Frowd

Communications of the IIMA

Legacy software is becoming increasingly common, and many companies nowadays are facing the challenges associated with this phenomenon. In certain circumstances, re-engineering is the only logical way to deal with legacy software. Such projects, by their very nature, are subject to a wide variety of risks. The aim of this study was to begin building the basis of a risk framework that will support future re-engineering projects within Agile (Scrum) environments. An interpretive case study approach has been followed, where the case study was the first phase of a re-engineering process, with the method of analysis being inductive and reflexive …

Enhancing Email Spam Detection Through Ensemble Machine Learning: A Comprehensive Evaluation Of Model Integration And Performance, Najah Al-Shanableh, Mazen S. Alzyoud, Eman Nashnush

Communications of the IIMA

Email spam detection and filtering are crucial security measures in all organizations. It is applied to filter unsolicited messages; most of the time, they comprise a large portion of harmful messages. Machine learning algorithms, specifically classification algorithms, are used to filter and detect if the email is spam or not spam. These algorithms entail training models on labelled data to predict whether an email is spam or not based on its features. In particular, traditional classification machine learning algorithms have been applied for decades but proved ineffective against fast-evolving spam emails. In this research, ensemble techniques by using the meta-learning …

Cyber Threat Intelligence Sharing In Nigeria, Muhammad Abubakar Nainna, Julian Bass, Lee Speakman

Communications of the IIMA

Cybersecurity challenges are common in Nigeria. Sharing cyber threat intelligence is essential in addressing the extensive challenges posed by cyber threats. It also helps in meeting regulatory compliance. There are a range of impediments that prevent cyber threat intelligence sharing. We hypothesise that we want to maximise this cyber threat intelligence sharing to resist malicious attackers. Therefore, this research investigates factors influencing threat intelligence sharing in Nigeria's cyber security practitioners. To achieve this aim, we conducted research interviews with 14 cyber security practitioners using a semi-structured, open-ended interview guide, which was recorded and transcribed. We analysed the data using an …

Enhancing Youtube Spam Detection, Sai Charan Pesaru

Electronic Theses, Projects, and Dissertations

This culminating experience project investigated various methods for enhancing spam detection on YouTube, a prevalent issue impacting user experience and platform integrity. The research questions addressed were: Q1) How do different spam detection methods compare regarding robustness, efficiency, and accuracy? Q2) What role do deep learning approaches like RNNs and CNNs play in improving spam comment identification? Q3) What are the unique benefits of using deep learning models for spam comment identification on YouTube? Q4) How can machine learning models be optimized for real-time spam detection on YouTube?

The study gave adequate findings that explained each research question. In the …

Ai-Driven Cybersecurity Threats And Organizational Consequences, Apeksha Kale

Electronic Theses, Projects, and Dissertations

ABSTRACT

This project used a case study research strategy to investigate the impact of AI-driven cybersecurity threats on organizations. The research questions are: Q1: How can different types of organizations improve their defenses against AI-driven cybersecurity attacks? Q2: How will future hackers most likely access AI tools, and what AI tools will they use? Q3: What strategies can organizations implement to enhance resilience against phishing emails? Three Case Studies were selected and analyzed to answer the three research questions. The findings are Q1: AI-driven cyberattacks pose significant risks, but organizations can improve defenses by investing in AI technologies like Vectra …

Corn Leaf Disease Prediction Using Deep Learning, Meghana Varayuri

Electronic Theses, Projects, and Dissertations

Corn is a widely cultivated agricultural product, serving as a cornerstone in food production and industrial applications such as biofuels, playing a crucial role in the global economy. This study explores the application of deep transfer learning to accurately classify major corn diseases from leaf images, aiming to enhance disease management strategies for improved agricultural productivity and sustainability. The customized Dense net 201 model achieved 95% prediction accuracy on an untrained dataset. Data augmentation improved the model’s accuracy from 91% to 95%. This supervised learning approach enhances the model’s performance by increasing the diversity, leading to better generalization and accuracy. …

Challenges The Sporting Industry Faced During Covid-19 On Fan Attendance: The National Football League, Tinika Hughes

Electronic Theses, Projects, and Dissertations

When COVID-19 struck, the world was in disarray for any sporting event. The Culminating Experience Project investigated the impact of COVID-19 on the National Football League (NFL). The research questions were: Q1: How were the NFL stadium attendances impacted before the COVID-19 closures (2016 – 2019)? Q2: How were the NFL stadium attendances impacted due to closures during the COVID-19 stadium closures (2019 – 2021)? Q3: How were the NFL stadiums impacted post-COVID-19 (2021 – 2023)? The data collected covers all 32 NFL teams from 2019 to 2023, including attendance rates on and off the field influencing factors.

The findings …