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The Polarization of NFTs: Association between Personality Traits and Perceived Value
Get PDFThe rise of Internet 3.0, the metaverse, and virtual realities is accelerating the shift from a physical economy to one that is digital, decentralized, and globally accessible. While the benefits and detriments of virtual assets like non-fungible tokens (NFTs) have received attention, individuals’ opinions about them remain polarized. This study investigates how personality traits shape users’ perceived value of NFTs. Using survey data from 805 respondents, we examine how the Big Five traits (openness, conscientiousness, extraversion, agreeableness, and neuroticism) are associated with 14 value dimensions spanning technology, art, and product aspects. The findings indicate that perceptions of NFTs vary among users. Of note, individuals high in agreeableness and conscientiousness perceive NFTs more favorably across the spectrum of value dimensions, whereas those high in neuroticism exhibit opposite tendencies. Extraverted individuals are drawn to the subjective norms and financial gains related to NFTs, while those high in openness value their information transparency
Empowering the Rural Consumer: The Role of Self-Service Support Models in Enhancing Customer Satisfaction with Satellite Internet Services
Get PDFRural broadband policy has largely focused on infrastructure deployment, yet the design of service support models can also have a significant impact on user satisfaction. In high-friction rural-remote regions—defined as areas with limited technician access, low provider density, and significant logistical barriers—support structures shape not just usability, but the lived experience of autonomy. This paper thus develops a conceptual model linking support model type (self-service vs. human-assisted) to customer satisfaction, mediated by perceived autonomy. Drawing on Self-Determination Theory (SDT) and the Self-Service Technology (SST) literature, and informed by qualitative fieldwork in Rockingham County, Virginia, we theorize that self-service models foster autonomy and thus improve satisfaction—particularly when users own and manage their broadband hardware. This conceptual paper lays the theoretical foundation for understanding how support model design—not just connectivity—shapes customer satisfaction in rural and underserved contexts
Introduction to the Minitrack on Beyond Digital Divide to Digital Literacy, Digital Fairness, and Ethical Futures
No full textDecision Criteria for Selecting Data Infrastructure Design Options in the Private Sector
Get PDFData infrastructures are foundational for creating economic and social value through data sharing. Yet, building sustainable and long-lasting data infrastructures for inter-organizational data sharing in the private sector remains a complex undertaking. A key challenge lies in designing data infrastructure services that reconcile the diverse requirements of the actors involved, including the choice of suitable architectural options (e.g., centralized versus decentralized). Drawing on a two-cycle Design Science Research approach with European practitioners, we propose a set of core decision criteria for data infrastructure design options in the private sector. Our study advances IS literature by enriching design knowledge on private data infrastructure services. Additionally, we improve the understanding of private data infrastructures by delimiting their core concerns from those of general software systems. Practitioners can apply our findings in the design and development of data infrastructures to make more informed decisions
A Dynamic Capabilities Perspective on Restaurant Demand Forecasting: Insights from a Large Restaurant Chain
Get PDFThe previous literature on restaurant businesses provides limited insight into demand forecasting. As a theoretical foundation, this research adopts a dynamic capabilities perspective on demand forecasting. From this viewpoint, restaurants possess different levels of demand forecasting capabilities. This study enhances a range of these capabilities across various market scenarios by utilizing different data and analytics tools. By introducing the dynamic capabilities perspective, this research aims to help restaurants assess their demand forecasting capabilities and provide strategic guidance for developing optimal demand forecasting strategies, considering both the technical and organizational resources available within and outside their business, as well as various market conditions
The Foundation-Hierarchy-Boundaries Framework: Architectural Factors Affecting Modular System Clarity
Get PDFModular systems are fundamental to information systems architecture, yet the factors contributing to their intuitive clarity remain underexplored, limiting their effective adoption. Design systems are a particular example of a modular system and they have become critical for managing UI complexity in information systems, yet lack scientific validation despite widespread industry adoption. This paper presents an empirical evaluation of the Atomic Design system through a mixed-methods study with 18 novice designers including a systematic qualitative analysis and the comparison of inter-rater reliability measures. Based on the findings, the paper derives the Foundation-Hierarchy-Boundaries (FHB) Framework for clarity in modular system architecture. The FHB Framework identifies three architectural factors of modular system clarity: (i) Foundation (accessible modular base components), (ii) Hierarchy (flat architectural structures of modular components), and (iii) Boundaries (clear categorical distinctions between types of modular components). We demonstrate the framework's application through evidence-based design guidelines for design systems, providing both theoretical contribution to modularity research and practical guidance for practitioners
Securing Python Supply Chain: Using Graph Theory for Vulnerability Prediction
Get PDFThe open-source Python ecosystem is a complex system whose community-driven nature creates significant software assurance challenges. This study develops a foundational, graph-theory-based technique for proactively predicting vulnerability in these critical supply chains. Modeling the Python Package Index (PyPI) as a directed graph of 411,056 packages, we evaluate how network centrality metrics, such as indegree, betweenness, and PageRank, serve as proxies for security risk. Our analysis demonstrates their significant power to predict both direct and indirect, dependency-propagated vulnerabilities. The results reveal how a few high-centrality nodes can cascade risk across thousands of downstream packages. This approach provides a scientific basis for ensuring system integrity, enabling new tools and practices to identify systemic weaknesses before exploits occur, and bridging the gap between collaborative innovation and robust cybersecurity
Coping Strategies for Tensions Between Digital Data and Data Practices in Data-Driven Organizations
Get PDFThis paper examines how workers cope with digital data tensions in railway operations at a safety-critical and data-intensive transport organization in Sweden. We show that the material properties of digital data and data practices create tensions in work practices, resulting in technostress under high accountability and time pressure. We identify five coping strategies: workarounds, negotiations, modifications, compromises, and abandonments. These strategies are sociomaterial; blending technical fixes, tacit expertise, and informal networks, and show how workers enact data in practice. Our findings contribute an empirical account and understanding of digital data tensions in railway operations, demonstrating that coping enhances workflows and data quality, but introduces invisible labour and new complexities. Building on these findings, we extend theoretical discussions on digital data tensions, technostress and coping, and propose conceptualizing data as dialogue, where meanings are continuously negotiated, and as a capacity-building tool, where coping generates organizational learning and resilience over time
PatientLens: AI-Enabled Interactive Avatars for Patient Report Summarization and Visualization
Get PDFIn clinical environments, doctors often must review large amounts of patient reports prior to consults and check-ups --- a task that is time-consuming, cognitively taxing, and prone to errors. We investigate how to improve this workflow via the use of AI-driven virtual avatars that enable clinicians to query, and summarize information from text-based patient reports. While the use of AI (specifically LLMs) brings significant potential benefits for clinical settings, it also presents critical challenges such as hallucination. Based on robust discussions and iterative prototyping with clinicians, we develop a human-in-the-loop approach that supports interactively creating and refining virtual avatars that visually present patient information while efficiently supporting LLM oversight and transparency. Evaluations help validate that the developed tool, nicknamed PatientLens, supports clinical review and summarization workflows. We also discuss how lessons learned during this project can enhance healthcare communication, optimize clinical workflows, and support improved health equity and outcomes
Tackling Managerial Cognitive Limitations by Harnessing Artificial Intelligence
Get PDFHuman cognitive limitations pose significant challenges to processing the vast amounts of data necessary for strategic decision-making and management. This paper examines some of the limitations, including heuristics and biases, and explores how emerging Artificial Intelligence (AI) systems may transform strategic management practices in the future. Drawing on previous literature and industry insights, we trace how AI tools are already reshaping strategic decision-making to overcome human limits. Our paper reveals a tension: while novel AI excels at pattern recognition, explanation, content creation, and predictive analytics across vast datasets, many strategically critical decisions, particularly those involving ethical judgment and authentic relationship building, are likely reserved for humans. This suggests that future leaders must balance AI's analytical power with distinctly human capabilities, and humans benefit from adapting to and collaborating with machines in decision-making processes. The paper concludes by proposing six working hypotheses and delineating research opportunities for understanding human-AI collaboration in the future