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Exploring Blockchain Applications Across the Green Hydrogen Supply Chain: Use Cases, Functionalities, and Implementation Potentials
Get PDFTo combat environmental pollution from excessive fossil fuel use, efforts are underway to shift toward carbon-free energy. Green hydrogen is seen as a crucial element of the energy transition. However, building a functioning hydrogen economy remains challenging, particularly in verifying renewable origin, managing certification processes, and enabling trustworthy data exchange across supply chain actors. Blockchain technology, in particular, offers significant potential to enable secure, decentralized, and verifiable data management within green hydrogen systems. This paper explores its application through a systematic literature review and an in-depth case study of selected industry projects. The results are synthesized into four application clusters that capture distinct functional roles of blockchain across production, certification, distribution, and consumption stages of the green hydrogen value chain
How AI Assistant Intelligence and AI Literacy Shape Technostress and Resistance: An Experimental Study
Get PDFThis study investigates how the intelligence of AI assistants and users’ AI literacy interact to influence resistance behavioral intention through technostress (i.e., eustress and distress). Using a 2×2 experimental design, we examine both eustress and distress as dual emotional pathways. The results show that emotional responses vary depending on users’ AI literacy, even under identical stimuli. These findings provide empirical support for the Stimulus-Organism-Response (S-O-R) model and offer practical insights for designing user-centered AI deployment strategies in organizational environments
Seven Axioms on the Nature of Generative AI: Laying the Foundation for a Genuine Understanding of Machine Intelligence
Get PDFWe challenge tendencies to evaluate Generative AI (GenAI), and large langue models (LLMs) in particular, through the lens of human intelligence, arguing that such anthropomorphic renditions constitute a fundamental category error. We do not dispute LLMs' capabilities, but contend that analogies to human intelligence obscure its true nature. This leads to unrealistic expectations and risks overlooking unhuman-like capabilities. We propose seven axioms to characterize GenAI and LLMs: (1) no direct relationship to truth, (2) no connection to the physical world, (3) absence of subjectivity, (4) lack of temporality, (5) no intentionality, (6) purely relational information representation, and (7) complex pattern prediction. These axioms, informed by existential philosophy, linguistics, and cognitive science, reveal GenAI as an "alien intelligence" fundamentally different from human cognition. We argue for developing a genuine machine psychology that embraces GenAI's alien nature rather than constraining it within human frameworks, to unlock applications that leverage its unique capabilities
Augmentation Innovation Paradox: Rethinking Validation in Generative AI-Driven Open Innovation
Get PDFOpen innovation is undergoing a fundamental transformation with the rise of artificial intelligence, particularly through the emergence of augmented innovation, characterized by the collaborative interplay between human creativity and generative AI. This study investigates a central tension in this shift: the ideation–validation paradox. While generative AI (GenAI) significantly accelerates the process of idea generation, it simultaneously introduces new complexities around validating the quality, novelty, and feasibility of those ideas. To address this paradox, we propose an extended ideation framework that identifies five socio-technical mechanisms aimed at making GenAI-driven contributions more systematic, auditable, and responsible. We conclude by outlining a future research agenda to advance our understanding of human–AI collaboration within open innovation ecosystems
VizCoach: Designing an Orchestration-Based Tool for Data Visualization Education
Get PDFVisualization courses are commonly found in university settings, but many students (particularly those from computer science or STEM backgrounds) struggle learning the ``design thinking'' aspects of visualization. In this paper, we investigate how to design and engineer a technology-enhanced learning platform for teaching visualization concepts called VizCoach. VizCoach adopts a hands-on, orchestration-based approach that abstracts away the coding aspects of visualization construction, allowing students to focus on applying design thinking principles during learning. VizCoach also supports instructors by providing workflows tailored for creating and moderating learning activities. Empirical evaluations help validate that VizCoach supports design and engineering requirements for successful orchestration and design thinking learning scenarios. We also discuss how tools like VizCoach contribute to technology-enhanced learning for visualization, and can provide opportunities for future research into visualization learning processes
Information Strategy in Platform Competition
No full textDigital platforms face increasingly intense competition, making the development of effective information strategies to gain competitive advantage a critical challenge. We develop a novel competition-based information value framework to examine how competitive dynamics influence the effectiveness of different information cues (factual, emotional, and attractiveness-based) in platform environments. Grounded in the Ability-Motivation-Opportunity (AMO) framework, we theorize that in competitive environments, factual cues negatively impact performance, while emotional and attractiveness cues generate positive effects. As competition intensifies, the influence of factual and emotional cues diminishes, while the impact of attractiveness cues strengthens; conversely, in non-competitive environments, factual cues enhance performance, whereas emotional cues exhibit inhibitory effects. Empirical analysis of 1.4 million projects from online crowdfunding platform supports our hypotheses. This framework advances understanding of how platform competition shapes optimal information strategies while providing practical insights for platform managers and participants to optimize managerial decision-making processes
When One Size Does Not Fit All: A Systematic Literature Review and Taxonomy of Multidimensional Maturity Models
Get PDFThis paper presents the findings of a systematic literature review (SLR) of 47 peer-reviewed studies on digital maturity models, focusing on their multidimensionality. First, it introduces a taxonomy that distinguishes Multi-Aspect Coverage (MAC) from Interdependent Multidimensionality (IMD), which employs formal weighting to reflect dimension-specific priorities and thereby improves diagnostic accuracy. Second, it reconfigures 977 indicators into 29 sub-dimensions and nine overarching dimensions through a deductive-inductive coding procedure. Third, drawing on contingency theory, the discussion offers context-sensitive implications for selecting an aggregation stance, thereby offering actionable guidance on balancing methodological rigor with pragmatic considerations. Hence, this review offers a systematic delineation of weighting processes in digital-maturity models, providing an operational boundary between MAC and IMD
Self-Modifiable Reversible Circuits
Get PDFSelf-modifiable reversible circuits are introduced. The purpose of these circuits is twofold: first, reversible circuits can reduce the energy consumption in a digital computer by orders of magnitude. Second, self-modifiable reversible circuits provide computational advantages in cybersecurity and in AI. Our focus is on integrating quantum randomness into reversible circuits that implement cryptographic algorithms. These new circuits hinder Eve listening to the hardware computations because the physical locations of the bit computations are capable of moving randomly across different parts of the circuit. These circuits are likely to hinder Eve from capturing cryptographic keys even if she uses acoustic, electromagnetic, or power cryptanalysis. At the hardware level, these circuits also provide new obfuscation tools that help keep an algorithm private
Using Knowledge Graphs to Test for the Presence of Hallucinations in Closed RAG-based and Custom GPT-based LLM Systems
No full textMany companies and government agencies are building large language model (LLM) systems for proprietary or specialized uses with sensitive data. These closed systems are not permitted to access the internet as there are concerns about data leakage. At the same time, the absence of internet access limits the data to data contained in the vector database which is used to implement retrieval augmented generation (RAG) or the ingested data in a Custom Generative Pre-trained Transformer (GPT). These systems wish to present authoritative responses and avoid hallucinations, factually inaccurate responses to various prompts. In sensitive government and enterprise environments, hallucinated responses can lead to serious consequences, including misinformation, policy errors, or breaches of trust, making their detection and prevention a critical priority. Using knowledge graphs, we can graph the data or parts of the data and use patterns to predict the areas to test where prompts may create these hallucinations in closed systems. Given the very large number of possible prompts for such a system, being able to target the testing of these systems is extremely important. Once issues are discovered, these graphs can also help developers fix issues where data is missing, erroneous, or must be enhanced using data curation