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Structure Preserving Dynamic Graphs for Power Systems
No full textLarge-scale integration of renewable energy resources presents the challenge of coordinating the output of numerous small generators and loads. This coordination problem typically involves solving a large centralized optimization problem. The growing number of decision variables associated with renewable resources increases the computational complexity of this coordination problem. Several approaches address this issue by decomposing the centralized problem into smaller, more computationally tractable subproblems. In this work, we extend the Kron-reduced dynamic graph to a structure-preserving model using the framework of non-uniform Kuramoto oscillators. We demonstrate how slow coherency can be used to identify groups of dynamically coherent nodes on the IEEE 14-bus test system, which can then serve as a basis for decomposing the centralized coordination problem
Creativity in Human-AI Co-Creation: A Two-Stage Model and the Da Vinci Score
Get PDFStandard collaborative systems for creative problem-solving and innovation often focus on process efficiency rather than the content of ideas. Generative artificial intelligence (GenAI) promises a shift: it can actively generate and evaluate ideas, potentially transforming innovation workflows. Integrating concepts from group support systems and collaboration engineering, we propose a two-stage model of human–AI co-creation. In Stage 1 (AI-seeded ideation), the AI rapidly establishes a foundation by generating knowable ideas; in Stage 2 (human-enhanced ideation), humans engage in unpatterned ideation using this AI-generated output, both refining initial ideas and introducing content gains/losses that extend traditional process gains/losses frameworks. We further define the Da Vinci Score as a composite creativity metric that enables differential weighting of criteria, aligning evaluation with business relevance and practical collaboration. Our three-condition hypotheses (AI-only, Human-only, Hybrid), partially supported by prior data, highlight implications for innovation practice, process design, and future research in business contexts
Navigating the Paradox: The Dynamic AI Trust Framework for Ethical Persuasion in Digital Marketing
Get PDFGenerative Artificial Intelligence (GenAI) is reshaping digital marketing by enabling more personalized content and persuasive user experiences at scale. While these capabilities offer significant value to marketers, they also heighten consumer concerns about data privacy, algorithmic transparency, and the authenticity of AI-generated content. This paper introduces the Dynamic AI Trust Framework, a conceptual model that explores how GenAI’s persuasive functions interact with consumer trust and privacy perceptions in digital environments. The framework outlines five interrelated dimensions — algorithmic transparency, user control and agency, data sourcing and security, fairness and bias mitigation, and authenticity and disclosure — as critical components for designing trustworthy and ethical AI-driven marketing strategies. By clarifying how these dimensions operate in practice, the framework provides a foundation for balancing innovation with responsibility, helping brands engage consumers more effectively while respecting their evolving expectations around data and trust
Introduction to the Minitrack on Practitioner Research Insights: Applications of Science and Technology to Real-World Innovations
No full textThe Path to Comprehensiveness: An LLM-Enhanced Systematic Literature Review on the Innovation Mindset
Get PDFThe study of the innovation mindset is not a new endeavor within and outside business and management. However, most of the studies and meta-analyses that have been undertaken on the topic rely on manual coding or simple keyword filters, thereby possibly missing some key artifacts due to the sheer scope of the daunting task. In this work, we try to overcome the comprehensiveness problem by introducing a multi‑LLM ensemble pipeline that integrates DeepSeekR1, Llama3, and QWEN models to retrieve, classify, and thematically cluster scholarly articles. Applying the pipeline to 106 peer‑reviewed publications, we identify four recurrent themes: (A) Creativity‑Risk Synergy, (B) Innovation Capacity, (C) Entrepreneurial Orientation, and (D) Adaptability and Problem Solving. These combinations improve over author‑supplied keywords, demonstrating the methodological value of using LLM models. The identified themes clarify the key needs for further research into the innovation mindset and offer an agenda for future explorations in information systems sciences
Introduction to the Minitrack on Impacts and Challenges in Engaging AI and Digital Humans: Human-Technology Collaboration
No full textThe Challenges of Balancing AI Compliance and Technological Innovations in Critical Sectors: A Systematic Literature Review
Get PDFThe rapid integration of artificial intelligence (AI) into critical infrastructure including healthcare, finance, energy, and defense, offers transformative benefits but also conflicts with evolving regulatory and governance frameworks. This paper presents a systematic literature review (SLR) to examine the challenges of balancing AI compliance and technological innovation across critical infrastructure sectors. The review follows established SLR guidelines to extract and synthesize insights from peer-reviewed articles, report, and institutional sources published between 2020–2025. The study identifies three interrelated challenges: fragmented regulations, excessive compliance burdens for smaller to medium enterprises (SMEs), and misaligned governance models. To address these challenges, the study highlights practical governance strategies, including risk-tiered regulation, compliance-by-design, and explainable AI, to support scalable and trustworthy AI deployment in critical sectors. Key contributions include a concise mapping of core AI-governance challenges and a conceptual diagram illustrating their overlap, as well as actionable strategies for policymakers and practitioners to harmonize oversight with innovation
Co-Designing a Virtual Reality System to Support Cognitive Behavior Therapy for Social Anxiety
Get PDFSocial anxiety disorder is one of the most common mental health disorders and is characterized by a fear of or avoidance of social situations. Standard treatment recommendations for this disorder include cognitive behavioral therapy (CBT). Virtual reality (VR) shows promise in improving CBT treatment, but it is necessary to understand how to design VR systems that can identify specific fears and effectively address safety seeking behaviours which play a critical role in maintaining the problem. This paper presents a co-design process and design evaluation of a VR system for this purpose, along with an evaluation of its treatment efficacy. Collaborative co-design sessions with experts in social anxiety, digital health, and game development were conducted and the design of the subsequently developed VR system was evaluated with ten participants. Based on the findings from the co-design activities and the design evaluation, this paper contributes system requirements for the development of a VR system to support CBT treatment for individuals with social anxiety disorder. The following therapeutic evaluation of the system with seven participants with reported levels of clinically relevant social anxiety symptoms showed promise that treatment in VR can potentially be used effectively to treat safety-seeking behaviors
Acceptability of Chatbot Support for Older Adolescents Involved in Cyberbullying
Get PDFChatbots may be effective tools to address cyberbullying among adolescents, but little research assesses their acceptability. To address this gap, we conducted 12 focus groups with U.S. adolescents (15-18 year-olds) to determine the acceptability of a hypothetical chatbot providing support for adolescents experiencing cyberbullying. We conducted qualitative content analysis using categories from the theoretical framework for acceptability. We find adolescents generally described the chatbot as acceptable, with the idea of such an intervention conjuring positive affect and expectations that it would be effective for perpetrators and victims and reduce the burden for seeking help. However, we also find evidence adolescents would hesitate to use such a chatbot due to ethical concerns, including whether the financial interests of the chatbot developers align with the wellbeing interests of adolescents. Chatbot-driven interventions for cyberbullying appear acceptable to adolescents, but it will be important that they be developed to prioritize wellbeing over other interests