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 cryptographic security—to key project management functions. Results indicate that blockchain adoption is strongest in integration, communications, quality, procurement, and stakeholder management, where coordination complexity and multi-party verification requirements are highest. Adoption remains largely conceptual or experimental, with limited operational deployment beyond construction-oriented contexts. Building on these findings, the study develops a three-layer integrative framework that conceptualizes blockchain as an organizational coordination capability connecting technological resources, management processes, and performance outcomes. The framework provides decision support for project management offices and technology leaders seeking to evaluate blockchain use cases, implementation readiness, and governance implications. This research contributes to the information systems literature by advancing understanding of technology–process alignment while identifying critical gaps and future research directions in blockchain-enabled project management.
The on-going Russo-Ukraine war (February 2012-present), the recent India-Pakistan war (May 7-10, 2025), and the Israel-US attacks on Iran (June 13, 2025) have become interesting for a variety of reasons, and garnered a lot of attention in international defense publications and weapons manufacturers. To many defense analysts and defense industry professionals, these wars have become real-life testing platforms to examine and evaluate various present-day war technologies. They have opened up discussion on the future of war, the increasing use of inter-connected networks in warfare, and the increasing use of remote-operated drones in conducting surveillance and in offensive actions against enemies. Satellite communication and visualization, the use of computer networks, and electronic jamming devices are increasingly becoming akin to munitions. It will be interesting to analyze the nature of present-day warfare by using an information systems and/or socio-technical systems perspective. It would be revealing to determine aspects of “networked-warfare.” Several questions come to mind, such as: What are the frameworks used? What are its components? How is the tracking and networking of very fast-moving projectiles accomplished? How are troop movements planned during combat? What is the typical network medium? What is the role of satellite communications in this type of warfare? What are some of the technical obstacles such as latency and interoperability, and how are these overcome? What is the role of AI and ML, if any, in modern weapons systems? There are also ancillary questions, such as: What are the ethical and social implications of new technologies such as drones? What is the role of disinformation and misinformation that are increasingly used in modern warfare? This research is an attempt to answer some if not all of these questions.
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. These contrasting patterns reveal a clear supply–demand imbalance during crises: funders are more willing to contribute, while creators are less inclined to initiate projects. This gap highlights opportunities for crowdfunding platforms to implement strategic interventions that support entrepreneurs, leverage funders’ increased engagement, and strengthen crowdfunding’s role in financial resilience.
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 manpower productivity, but negatively associated with occupancy rate. Telehealth demonstrates strong positive effects across all three efficiency measures, while HIE is positively correlated with occupancy rate. IT spending is positively related to capacity productivity but negatively related to manpower productivity. No significant relationship is observed between cybersecurity and performance outcomes. Originality/Value – This study contributes to healthcare management literature by integrating multiple dimensions of digital innovation into a unified framework and by enhancing understanding of how digital strategies shape hospital operations in the post-pandemic. Research limitations/implications – Future research can assess the long-term effect of digital investment, particularly in cybersecurity, on hospital performance. Practical implications - The findings suggest that hospital administrators should prioritize investment in automation and telehealth to realize immediate operational gains, while recognizing that cybersecurity investment may yield benefits through long-term institutional reliability rather than short-term efficiency. Social implications – Insights from this study support more equitable allocation of digital resources across hospitals, particularly in rural and underserved regions. Strategic digital adoption may improve continuity of care and overall performance of the healthcare sector.
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 tracing the formation and transformation of X fan communities during these sporting events by analyzing the influence of retweets, hashtags, and mentions. Using the Louvain algorithm, it provides a modularity-based approach in capturing and analyzing community structures and their interactions in real time. In total, community nodes such as 61561 and 10415 were identified during the FIFA World Cup and T20 World Cup, respectively. These findings have very actionable implications for sports marketers and professionals, providing them with a framework to leverage social connections in a strategic manner. This work goes beyond the insights of sport and will be useful in areas of social circles and management to help improve understanding of community dynamics toward strategic impact in a digital world.
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 challenging. Previous studies had not accounted for variables such as seasonal fluctuation in opinion and the impact of specific service features on customer satisfaction. This study fills the gaps by employing sentiment analysis tools, including TextBlob and VADER (Valence Aware Dictionary for Sentiment Reasoning), to analyse customer opinions from platforms like X, Skytrax, and TripAdvisor. The study process involves data collection through automated web scraping tools, followed by data cleaning and text analysis to tag sentiments and identify variables influencing customer emotions. Key findings indicate that carriers like IndiGo and Vistara are highly rated by customers for punctuality, service quality, and operational consistency. Conversely, Air India and SpiceJet are faulted for delays and inefficiency. Seasonal patterns further indicate that negative sentiments are greater during peak travel seasons. The study highlights the importance of customer-centric initiatives, operational efficiency, and advanced analytics to boost brand loyalty in a highly competitive market.
Digital piracy is a form of copyright infringement, and challenges persist in addressing it effectively. Accordingly, understanding why people engage in digital piracy is crucial. Although prior studies have examined digital piracy from multiple perspectives, existing studies on the explanatory factors of digital piracy remain fragmented. To address this research gap, this study develops an integrated model that incorporates key theoretical perspectives, neutralization theory, social learning theory, and the theory of planned behavior (TPB), along with key determinants including gender, age, and the technology factor. Rather than conducting a meta-analysis of previous studies, this study adopts a survey-based approach to examine the effects of these factors on digital piracy. We collected our data through a survey and used t-tests, ANOVA, and logistic regression to analyze it. The results indicate that gender, age, the neutralization factor, and the social learning factor have significant effects on digital piracy. Specifically, gender, the neutralization factor, and the social learning factor play a crucial role in the use of BitTorrent for engaging in digital piracy. In contrast to prior research, this study shows that the technology factor does not have a statistically significant influence on digital piracy. This study advances digital piracy literature by offering an integrated model and a comprehensive analysis of the factors influencing digital piracy, thereby addressing the limitations of prior fragmented research that focused on a narrow set of factors and theoretical perspectives. Practically, by integrating these findings, administrators and policymakers can develop more precise interventions to discourage digital piracy, ultimately reducing digital piracy behaviors.
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 trade-off challenges the widely cited “female advantage” in crowdfunding. Further analyses offer actionable insights: female entrepreneurs should target new backers to increase engagement and emphasize prior fundraising experience to boost pledge size. This study offers theoretical and practical insights into improving women’s crowdfunding outcomes by accounting for stereotype-based perceptions.
Advances in digitalization have accelerated consumer migrations away from traditional cash- and check-based payment systems towards electronic payment systems worldwide. Yet, many people still remain resistant. This study examined the role of motivational and inhibiting factors and their interaction effects on the use of e-payment systems. The findings suggest that perceived cybersecurity risk, risk propensity, trust in the companies offering the e-payment services, and technology readiness have a significant direct impact on the use of e-payment systems. The findings also suggest that risk propensity and technology readiness have a tempering effect on the negative impact of perceived risk. The study discussed the practical and theoretical implications of the research.
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, these results underscore the importance of early interventions, mentorship, and institutional support systems in fostering equitable STEM participation and cultivating the digital competencies essential for organizational transformation.
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 system confirms the impact of XAI in reducing the undelivered product returns from 22.5% to 6.34%. The study illustrates how combining XAI with predictive modeling can drive the reengineering of business processes, ultimately reducing product returns.
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 cloud—combining public and private cloud services—offered a more balanced path towards operational efficiency, enhanced security, faster application development, improved business insights, and increased resilience (Raynovich, 2023). This research aimed to explore the evolving landscape of public cloud services, identify emerging trends, and assess the shifting dynamics between public and private cloud environments. Problem Statement and Research Questions Adopting cloud computing required a well-defined IT strategy (Tripathi, 2022), one informed by a clear understanding of which services were best suited for public cloud versus those better kept in private cloud environments. The primary research question sought to guide organizations in navigating these complex decisions, ensuring that IT strategies included the necessary criteria for successfully placing services in the most suitable environments. Key considerations included: Security: Leveraging Platform as a Service (PaaS) offerings could shift the responsibility for patching operating systems to cloud providers, enhancing security. Network: Assessing current internet connectivity, client locations, and dependencies on private cloud services was crucial for a successful cloud strategy. IT Skills: Evaluating the skill set of existing IT staff was essential for determining whether management tasks should be retained in-house or outsourced to cloud providers. Continuous Delivery: Reviewing current Development/Security and Operations (DevSecOps) practices and exploring Agile/Scrum methodologies could improve development and operational efficiencies. A secondary research question investigated the concerns and hesitations of IT decision-makers when migrating critical services to public cloud providers, including the factors that prevented certain private cloud services from being migrated. Methodology The study utilized a descriptive research design, targeting IT professionals in decision-making roles. Data were collected through an online survey administered via the Qualtrics XM platform. The survey addressed primary and secondary research questions to capture insights into the current cloud computing landscape. Statistical tests were conducted to ensure the significance of the findings. Expected Outcomes This research aimed to identify the critical factors influencing the placement of services in public versus private cloud environments. It explored which services thrived in public or private clouds and the critical considerations for planning successful service migrations.
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 adoption and intention to adopt BT. While the complexity of BT does not significantly influence the level of adoption, it negatively impacts the intention to adopt BT. The study provides insights into key factors that managers need to focus on to accelerate the adoption of BT.
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 waste management, manufacturing and supply chain management, policy and governance, UN sustainable development goals, and public attitudes. Each identified study was reviewed within its category and the findings provide a description of the current relationship between AI and environmental sustainability along with directions for future research. In some studies, AI methods are used for analysis of large data sets to identify the factors that impact environmental sustainability outcomes. In other studies, they looked at strategies and regulations that enable effective AI use. Studies also note that AI can produce negative outcomes due to increased energy demand, potential for perpetuating bias in decision models, and violation of individual privacy rights.
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 countries, VR users showed greater enjoyment than TV users on both days, with similar effects seen in U.S. participants. However, no significant difference was observed between TV and VR groups for Nigerian participants on Day 1. Nigerian participants preferred VR to TV, while U.S. participants preferred TV for watching sports. Nigerian participants were also more likely to select a VR headset over TV for watching sports events, while U.S. participants tended to select TV over VR. The findings demonstrate the applicability of the media richness theory and affordances theory in the media technology context.
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 shipment delays proactively. A distinctive methodological innovation lies in explicitly integrating country logistics capabilities such as customs efficiency, infrastructure quality, and timeliness with internal shipment metadata, enabling a more comprehensive and precise prediction of delays. Empirical validation demonstrates that this integrative approach significantly enhances predictive performance, revealing systemic inefficiencies and enabling targeted managerial interventions. Consequently, logistics managers can leverage these insights to strategically optimize healthcare logistics and supply chains. This study contributes to a rigorously validated and scalable predictive tool, facilitating a strategic shift from reactive to anticipatory logistics management within global health supply chains.
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 times. This new understanding deepens our grasp of crowdfunding dynamics and highlights crowdfunding's vital role as a lifeline for entrepreneurs during major crises.
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.
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 of social influences – personal, clinical, and peer – were identified. Various dimensions and contexts were found to affect the PHCI-seeking behaviors of the baby boomers. Managerial implications of the results are presented.
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 address the challenges through unified data models, automated tools, and best practices, but it is still in need to develop more robust, standardized conceptual models that can accommodate the unique characteristics of decision-oriented data structures. In this paper, we propose a new approach to conceptual modeling for decision-oriented databases. This approach is knowledge-driven and decision-oriented. It provides a comprehensive view of data at the conceptual level for decision-oriented databases.
