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Who Matters in AI Compliance? A Stakeholder Framework for Enterprise Strategies
Get PDFAs artificial intelligence (AI) becomes central to enterprise operations, aligning AI strategies with regulatory developments is increasingly vital. This study proposes a stakeholder-involved maturity model to embed AI compliance into organizational processes. Design Science Research-oriented, the maturity model integrates findings from literature, expert interviews, a focus group discussion, and an evaluative single case study. Including a four-staged maturity assessment, 12 relevant stakeholders with specific responsibilities are identified to address AI regulation in enterprise AI strategies. Results show varying stakeholder relevance across maturity levels and highlight the importance of cross-functional collaboration across crucial department units. The model offers a triangulation of insights in a structured management framework. Based on a comprehensive stakeholder analysis and a consequential involvement intensity for AI regulation integration in enterprises, we develop and contribute to research by providing a structured and actionable framework for organizations aiming to implement and transition to increased AI compliance
Hacking Distributed Energy Resource Power Plant Infrastructure Using Reinforcement Learning
Get PDFAdvancements in Artificial Intelligence (AI) are enabling adversaries to more efficiently penetrate and navigate networks, posing new risks to critical infrastructure such as the electric power grid. This study evaluates three attack strategies—AI-based, Brute-Force, and Random—within the Network Attack Simulator (NASim), a synthetic environment designed for cybersecurity testing. The AI-based methods include Deep Q-Network (DQN) and Deep State-Action-Reward-State-Action (SARSA) reinforcement learning algorithms. Training results show that both AI approaches effectively learn to conduct subnet scans, service/process scans, and privilege escalation attacks to gain root access. During testing, AI agents completed their objectives in fewer than 28 actions, while Brute-Force and Random methods required over 200 actions. These findings demonstrate AI’s efficiency and potential to automate the launch of cyberattacks targeting Distributed Energy Resources (DERs), offering a baseline for future research targeting real-world networks
Analyzing Protocols of Information Granularity Allocation to Compute Missing Values in Intuitionistic Reciprocal Preference Relations
Get PDFIn the field of Granular Computing, a fundamental approach focuses on the management of incomplete intuitionistic reciprocal preference relations through the allocation of information granularity and the execution of a corresponding optimization procedure. It facilitates the transformation of numerical models into their granular counterparts, thereby providing a more accurate representation of reality to recover missing information. In decision-making contexts that involve intuitionistic reciprocal preference relations, this approach has proven essential in advancing procedures for estimating incomplete information. Nevertheless, while several protocols for information granularity allocation have been proposed, only one has been actively implemented thus far: the protocol based on a uniform and symmetric allocation of information granularity. To address this limitation, the objective of this study is to assess the effectiveness of existing protocols for allocating information granularity in the estimation of missing values in incomplete intuitionistic reciprocal preference relations. Numerical tests are included to demonstrate the efficacy of the protocols
The Hidden Cost of Cybersecurity Groupthink: A Case for Ethical Diversity
Get PDFAs cybersecurity decisions increasingly impact privacy, safety, and civil liberties, ethical reasoning becomes crucial for practitioners. Yet the field remains demographically homogeneous, potentially limiting ethical perspectives in critical decision-making. This study examines how team diversity influences ethical engagement in cybersecurity through semi-structured interviews with three U.S. professionals from teams with varying demographic composition. Using the Principlist Ethical Framework, we analyze how race, gender identity, neurodivergence, and sexual orientation shape ethical awareness across beneficence, autonomy, non-maleficence, justice, and explicability principles. Findings reveal that professionals from underrepresented backgrounds often demonstrate heightened ethical sensitivity and identify blind spots overlooked by homogeneous teams. However, diverse perspectives only translate to meaningful action when organizational culture supports ethical dialogue. This research suggests that strategic diversity hiring combined with supportive ethical cultures can strengthen both cybersecurity decision-making and organizational risk management
E-Participation Adoption: Case Studies of Supply and Demand-Side Dynamics in Rwanda
Get PDFThe paper explores the dynamics of e-participation adoption through the two main dimensions identified in the literature: the supply-side (government) and the demand-side (citizens). It focuses on how the interaction between supply- and demand-side drivers shapes e-participation adoption and influences meaningful outcomes, drawing on two case studies from Rwanda—the Citizen’s Guide to the Budget and Irembo.Gov. Through a thematic analysis, using both deductive and inductive coding strategies, the paper identifies the key themes that shape the intersection of supply and demand of e-participation. The case studies highlight that e-participation in Rwanda remains in its formative stages, characterized primarily by government-driven initiatives that often position citizens more as recipients rather than active contributors
Developer Productivity With and Without GitHub Copilot: A Longitudinal Mixed-Methods Case Study
Get PDFThis study investigates the real-world impact of the generative AI (GenAI) tool GitHub Copilot on developer activity and perceived productivity. We conducted a mixed-methods case study in NAV IT, a large public sector agile organization. We analyzed 26,317 unique non-merge commits from 703 of NAV IT's GitHub repositories over a two-year period, focusing on commit-based activity metrics from 25 Copilot users and 14 non-users. The analysis was complemented by survey responses on their roles and perceived productivity, as well as 13 interviews. Our analysis of activity metrics revealed that individuals who used Copilot were consistently more active than non-users, even prior to Copilot’s introduction. We did not find any statistically significant changes in commit-based activity for Copilot users after they adopted the tool, although minor increases were observed. This suggests a discrepancy between changes in commit-based metrics and the subjective experience of productivity
Capturing Authorship Style Through Large Language Models
Get PDFThis work explores the use of large language model (LLM) embeddings to capture style in authorship attribution tasks. By applying a Siamese network to embeddings from OpenAI’s text-embedding-ada-002, we assess whether style embeddings can distinguish authors across unseen texts. The results show a strong attribution accuracy that outperforms traditional characteristics, though the performance declines with more authors and improves with longer texts
Digitizing Intangible Cultural Heritage: An Ethnographic Exploration of Dabenqu’s Transformation into Online Engagement in Dali, China
Get PDFThis ethnographic research examines the digitalization of Dabenqu, a traditional Bai ethnic group folk singing practice, through the lens of the Zhao family’s adaptation to the opportunies posed by digital transformation. It explores how the Zhao family, as inheritors of this intangible cultural heritage, have navigated the intersection of tradition and digital innovation. The study traces the evolution of Dabenqu from its traditional village performances to its adaptation and dissemination via multiple digital platforms, including WeChat, Kuaishou, and Douyin, encompassing online performance videos, recorded rituals and ceremonies, and live streaming. The paper delves into the implications of digital media in preserving and transforming Dabenqu, highlighting both the opportunities and challenges in maintaining cultural integrity while reaching a global audience. This research offers new insights into the role of digital technologies in sustaining the vitality of this folk tradition and contributes to the broader understanding of the digitalization of intangible cultural heritage
Small College Technology Transfer - Options and Efficacy of Oversight Frameworks
Get PDFThis case study defines processes that were developed to initiate a technology transfer program at a university that was expanding beyond teaching and research to include commercialization opportunities for the faculty. The case analyzes how the university’s effort to support a particular research group triggered the development of technology transfer processes to be used across the institution. In addition, an analysis of institutions with technology transfer offices provides context for the case. Several models are applied to the case including Carayannis’ Quintuple Helix model and Chen’s stages of development. The conclusion provides insights on how the drivers to improve faculty culture and societal good opened up a new pathway sustainable with a leaner and more long-term funding model
When Language Backfires: The Strategic Limits of Linguistic Distinctiveness in Early-Stage Blockchain Startups
Get PDFIn the era of digital transformation, startups' visions are increasingly communicated through AI-analyzed language and signals derived from various platforms. This study examines how textual characteristics within startup descriptions influence funding outcomes in the Blockchain Venture Capital market. By leveraging NLP and topic modeling techniques on a dataset of 2,176 early-stage companies, we extract AI-derived features such as linguistic distinctiveness, competition intensity, lexical diversity, topic entropy, and disruption orientation. Grounded in signaling theory and optimal distinctiveness theory, our results indicate that Blockchain startups that use overly disruptive, complex, or unique language are less likely to secure continued investment. In contrast, firms that align their descriptions with competitive linguistic norms are more successful in obtaining venture capital funding rounds. This research contributes to the literature on digital entrepreneurship, optimal distinctiveness, and language-as-strategy by demonstrating how AI can reveal the subtle textual cues that shape investors’ perceptions and legitimacy in competitive funding ecosystems