Business Intelligence Commons^™

Wings Of Perception: Investigating Customer Sentiments In Indian Aviation Sector, Yashodhan Karulkar, Kaiwan Vaghchhipawala, Aditya Trivedi, Anushree Talekar

Journal of International Technology and Information Management

India's aviation sector, a key contributor to the nation's economy, has experienced rapid growth, supporting nearly 7.5 million jobs and contributing approximately $30 billion annually to the GDP (Gross Domestic Product). The growth, driven by increased demand for air travel and government incentives, has resulted in more competition among carriers. In the competitive market, it is necessary to understand customer preferences to enhance the quality of service and maintain a competitive edge. The dissemination of customer opinions on social media and review platforms offers airlines the opportunity to access passenger views. However, extracting useful information from this unstructured data is …

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 …

Too Warm To Win Big? Unpacking The Backer Dynamics Behind Female Crowdfunding Success Using A Warmth And Competence Perspective, Dan Liu

Journal of International Technology and Information Management

While crowdfunding is often heralded as a democratized funding avenue that empowers women with higher success rates, this study reveals a more nuanced picture of gender dynamics. The Stereotype Content Model suggests that women are often perceived as warmer but less competent. Using a large dataset from Kickstarter, we find that female-led projects can attract more backers, likely due to warmth-driven appeal, but receive smaller average contributions, potentially due to concerns about risk linked to lower perceived competence. However, the total funding raised by female-led campaigns is comparable to that of male-led ones, showing no clear advantage or disadvantage. This …

Online Community Dynamics: An Analysis Using Louvain During Major Sporting Events, Anushka Jaint, Yashodhan Karulkar, Kashish Jindal, Sri Sai Harshita Gadavarthi, Sanya Gulati

Journal of International Technology and Information Management

With the power of social media transforming the way people connect and interact with each other, the dynamics of community formation on platforms such as X during major events are of crucial importance. While social media is an increasingly key driver in determining interactions, little is known about the online influence forming and developing fan communities in high-stakes events. This study looks into the development of user communities for datasets drawn from Kaggle on two of the world’s largest sporting events: the FIFA World Cup 2022, or football, and the T20 World Cup 2022, or cricket, with the aim of …

Blockchain As A Digital Coordination Infrastructure For Project Management: A Systematic Review And Integrative Framework, Cherie Bakker Noteboom, Sai Neelima Seru, Aravindh Sekar

Journal of International Technology and Information Management

Blockchain technology has gained increasing attention as a digital infrastructure capable of improving transparency, trust, and coordination in complex, multi-organizational project environments. However, existing research on blockchain-enabled project management remains fragmented and industry-focused, providing limited guidance for organizational adoption and integration. This study addresses this gap through a systematic literature review of 29 peer-reviewed studies, following PRISMA guidelines, to examine how blockchain capabilities are incorporated into project management practices across industries and maturity stages.

Grounded in Resource-Based View and Coordination Theory, the analysis employs a feature-to-process mapping approach to link six core blockchain capabilities—decentralization, transparency, immutability, smart contracts, traceability, and …

The Value Of Personal Data Ecosystems: A Flemish Media Sector Case Study, Maarten De Mildt, Melanie Verstraete, Sofie Verbrugge, Didier Colle

Journal of International Technology and Information Management

Personal Data Stores (PDSs) have been proposed as a privacy-preserving approach to data sharing that increases individual control over personal data while enabling new forms of cross-organizational collaboration. This collaboration leads to the emergence of Personal Data Ecosystems (PDEs). Despite growing interest in PDEs, limited research has examined how the organizational and economic barriers identified in prior studies manifest in practice. This paper investigates these challenges through a case study of the Flemish media sector within the Solid4Media project, which explores the use of PDSs to support data sharing and personalization across media organizations. Using a qualitative research design, data …

Responsible People Analytics For Remote-Work Decisions: A Machine-Learning Benchmark For Classifying Perceived Productivity, Ruth Menjivar, Nima Molavi, Narges Mashhadi Nejad

Journal of International Technology and Information Management

This study examines whether employee-perception survey data can support responsible people-analytics decisions about remote-work productivity. Using the public New South Wales (NSW) Remote Working Survey 2021 (N=1,512), the study benchmarks statistical and machine-learning classifiers for self-reported perceived productivity classes (same, less, or more productive when working remotely relative to onsite work), not objective output, under default, class-weighted, and resampling protocols. Main evidence comes from 5×5 repeated stratified cross-validation using macro F1 and balanced accuracy with fixed model specifications. Class-balanced separability is modest. Random Forest, CatBoost, and LightGBM form a leading cluster with overlapping confidence intervals (macro F1 ≈0.51–0.52). Affective/well-being items, …

Towards Developing A Career Technology Fit Framework And Analyzing Its Influence On Work-Related Outcomes Among It Professionals, Gunjan Tomer

Journal of International Technology and Information Management

With growing attrition rate and significant demand for skilled IT professionals, the importance of studying their behaviour has become important for both academia and industry. Despite ample amount of research, there is still a gap between theory and practice. Based on our qualitative study conducted on Indian IT professionals we propose that technology allocation might contribute in understanding the behaviour of IT professionals. We found that IT professionals evaluate the technology allocated to them based on their individual career motives. This evaluation, either positive or negative, influences their job outcomes. Further, we explored the factors that make a technology preferable …

High Technology And The Developing State: The Arrival Of Supercomputers In India, Ramesh Subramanian

Journal of International Technology and Information Management

While India has made vast strides in information technology in the last few decades, its success is mainly attributed to its software, rather than its hardware sector. In fact, India’s attempts at developing computer hardware that can match international standards have largely been unsuccessful. A notable exception is its development of a series of supercomputers that match and exceed many international standards. This paper looks at an interesting period in India’s computing history – namely the 1980s and 1990s – focusing on its development of an indigenous supercomputer. During that period, supercomputers were thought to be the sole privy of …

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, …

Does Digital Innovation Matter For Hospital Efficiency? Evidence From U.S. Hospitals, C. Christopher Lee, Shihui Fan, Jung Young Lee, David W. Hwang

Journal of International Technology and Information Management

Purpose – This study examines the impact of digital innovation on hospital performance, providing evidence to guide healthcare administrators and policymakers in making informed decisions regarding digital investment.

Design/Methodology/Approach – Using data from the 2020 American Hospital Association (AHA) U.S. Hospital Survey and the 2019 AHA Information Technology Survey, we empirically analyze the relationship between five dimensions of digital innovation—automation, cybersecurity, telehealth, health information exchange (HIE), and IT spending—and three efficiency indicators: occupancy rate, capacity productivity, and manpower productivity.

Findings – The results show that digital innovation has varying effects on hospital efficiency. Automation is positively associated with capacity and …

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 Impact Of The Covid-19 Pandemic On National Hockey League Fan Attendance, Ramon E. Rivera

Electronic Theses, Projects, and Dissertations

The COVID-19 pandemic struck swiftly around the world, causing several societal functions and normalcies to adapt or shut down completely. This culminating project explores the effects of COVID-19 on arena fan attendance of the National Hockey League (NHL). This project was guided by the following three research questions: Q1 – How did the NHL arena closures affect fan attendance during the COVID-19 pandemic? Q2 – What factors influenced NHL fan arena attendance before the start of the COVID-19 pandemic? Q3 – What factors impacted the NHL arena fan attendance after the conclusion of the COVID-19 pandemic? An empirical model developed …

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, …

Challenges The Sporting Industry Faced During Covid-19 On Fan Attendance: The Case Of The National Basketball Association, Marlon Long

Electronic Theses, Projects, and Dissertations

This culminating experience project investigates the challenges the sporting industry faced during the COVID-19 pandemic, with a focused look at the National Basketball Association. The research questions are: (Q1) How did the NBA arena shutdowns impact fan attendance for each team during the COVID-19 pandemic? (Q2) What factors influenced NBA arena attendance before the COVID-19 shutdowns? (Q3) What factors influenced NBA arena attendance after the COVID-19 shutdown? The data collected includes all 30 NBA teams from 2019 through the 2024 seasons. The research questions were analyzed using multilinear regression analysis and comparison of attendance data over 6 seasons including the …

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 …

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 …

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 …

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 …

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 …

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 …

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. …

Understanding The Dynamics Of Chatbot Design, Transparency, And Ethical Implications In Customer–Brand Relationship In Hospitality And Tourism Industry, Shakera Parveen

Electronic Theses, Projects, and Dissertations

This culminating experience project investigated the impact of chatbot interactions on customer perceptions, ethical considerations, and transparency within the hospitality and tourism industry. The research questions are: Q1: how do the specific visual and verbal aspects contribute to shaping customer perceptions within the context of chatbot marketing efforts? Q2: what steps can companies take to address and mitigate ethical concerns surrounding interactions with chatbots? Q3: To what extent does the level of transparency and explainability in chatbot interactions influence customer trust and satisfaction? The findings are: Q1: the study revealed that visual and verbal aspects of chatbot design significantly shape …

Automatic Speech Recognition For Air Traffic Control Using Convolutional Lstm, Sakshi Nakashe

Electronic Theses, Projects, and Dissertations

The need for automatic speech recognition in air traffic control is critical as it enhances the interaction between the computer and human. Speech recognition helps to automatically transcribe the communication between the pilots and the air traffic controllers, which reduces the time taken for administrative tasks. This project aims to provide improvement to the Automatic Speech Recognition (ASR) system for air traffic control by investigating the impact of convolution LSTM model on ASR as suggested by previous studies. The research questions are: (Q1) Comparing the performance of ConvLSTM with other conventional models, how does ConvLSTM perform with respect to recognizing …

Association Between Food Insecurity And Chronic Health Condition Among Adults (18 To 48), Prasanth Reddy Guda

Electronic Theses, Projects, and Dissertations

Uncertainty or limited access to safe food, known as food insecurity, can affect the health and healthcare needs of individuals with several chronic conditions. During 2022, 12.8 percentage (around 17.0 million) of households in the United States experienced food insecurity. The main objective of this study was to examine the Association between food insecurity and chronic health conditions adults. We collected data from NHIS 2022 sample Adults’ Interview (The National Health Interview Survey). The research question asked are: (Q1) Are there certain long term health issues that are more common among adults facing food insecurity age in between the ages …

Impact Of Seasonality On Demand Forecasting Techniques For Small-Scale Food Retailers, Indu Sree Guturu

Electronic Theses, Projects, and Dissertations

This study explores the impact of seasonality on SKU (Stock Keeping Unit) demand forecasting in small-scale food retailers through the analysis of historical sales data, focusing on four key products, labelled: Product A, Product B, Product C & Product D. The research aims to assess the accuracy of different forecasting models in capturing seasonal fluctuations. The research questions are: (Q1) What is the impact of seasonality on SKU demand forecasting in case of small-scale food retailers. (Q2) How does the efficacy of Traditional model compare to alternative technologically driven forecasting techniques? (Q3) To what extent can the impact of seasonality …

Effectiveness Of Cnn-Lstm Models Used For Apple Stock Forecasting, Ethan White

Electronic Theses, Projects, and Dissertations

This culminating experience project investigates the effectiveness of convolutional neural networks mixed with long short-term memory (CNN-LSTM) models, and an ensemble method, extreme gradient boosting (XGBoost), in predicting closing stock prices. This quantitative analysis utilizes recent AAPL stock data from the NASDAQ index. The chosen research questions (RQs) are: RQ1. What are the optimal hyperparameters for CNN-LSTM models in stock price forecasting? RQ2. What is the best architecture for CNN-LSTM models in this context? RQ3. How can ensemble techniques like XGBoost effectively enhance the predictions of CNN-LSTM models for stock price forecasting?

The research questions were answered through a thorough …