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Generative AI-Powered Cybersecurity Threats: A Growing Concern
Get PDFWith the advancement of generative AI (GenAI) models, new cybersecurity risks have surfaced of which including threats such as GenAI-driven phishing and deepfakes. Exacerbating the issue herein is that existing security methods and frameworks largely struggle to address these evolving attack vectors, thus making the case for adaptive mitigation strategies. This study analyzes emerging GenAI cyber threats, real-world cases, and defense mechanisms, using thematic analysis to categorize risks and solutions. It highlights the need for further studies in developing advanced threat detection methods, fortifying AI governance, and interdisciplinary collaboration, and focusing on advancing explainable AI, adversarial training, and regulatory enforcement to balance security and innovation. This research aims to strengthen our defenses against GenAI cyber threats and safeguard our digital landscape
AI-Driven Improvements in Patient-Doctor Communication for Remote Consultations
Get PDFThe fast development of telemedicine has shown the value of medical chatbots in facilitating interaction between patients and doctors. This study examines ChatDoctor, a health chatbot optimized with LLaMA, to determine the medical diagnosis suggested by Artificial intelligence compared to a physician’s diagnosis. We employ 100,000+ user reviews and health chatbot conversations, applying Natural Language Processing (NLP) and Large Language Models (LLMs), to investigate patient interactions. Findings indicate that although chatbots improve access, patient engagement, and convenience, they also have accuracy and trust issues. Topic modeling identifies a contradiction in user experience, where positive comments highlight chatbot support and negative comments refer to response reliability problems. This research contributes to the Information Systems literature by demonstrating how LLMs enhance telehealth and provide valuable insights for improving chatbot design and patient-centered AI solutions. The results emphasize the need for sophisticated AI models that balance efficiency, clinical accuracy, and user demands
CYBERSECURITY SHOW-AND-TELL: AN ANALYSIS OF FIRM DISCLOSURE BEHAVIOR AND THE RELEVANCE OF PRIOR DATA BREACHES
Get PDFCybersecurity disclosures in reports filed by public companies subject to U.S. Securities and Exchange Commission (SEC) requirements provide investors with insights into firms’ cybersecurity incidents and risk management efforts, which are increasingly pivotal to investor decision-making. To improve the informativeness and consistency of these disclosures, the SEC introduced enhanced cybersecurity disclosure requirements in 2023, mandating detailed cybersecurity disclosure information in annual 10-K filings. In this study, we explore what these new disclosures reveal about firm cybersecurity behavior and communication practices. Specifically, we focus on (1) identifying general themes that reveal firms\u27 cybersecurity risk management processes, governance, and strategy, and (2) examining how past data breaches influence the prevalence of these reporting themes. Combining machine-learning tools and human qualitative analysis, our mixed-methods analysis reveals significant associations between past data breaches and specific themes, suggesting that firms with breach histories prioritize these areas in subsequent disclosures, signaling strengthened cybersecurity efforts to investors
Artificial Intelligence Governance: Legal and Regulatory Issues
Get PDFArtificial intelligence (AI), such as ChatGPT and DeepSeek, is reshaping society, presenting both opportunities and challenges. While AI enhances convenience, productivity, and innovation, it also raises concerns such as deepfakes, security risks, legal issues, and ethical dilemmas. Despite governments proposing principles to address these problems, there is insufficient scientific research on effectively regulating and governing the development and operation of AI. To address this gap, a qualitative case study was conducted, focusing on the legal and regulatory aspects of AI. Three senior legal experts—two experienced judges and a law school professor—were interviewed to gather insights on potential legal challenges and key factors for crafting AI-related laws and governance policies. This paper explores AI\u27s adverse legal impacts, regulatory needs, and the policies required to guide and monitor its development. The results of this study are valuable to legal professionals, businesses, policymakers, and the general public
Redução de acidentes de trânsito e óbitos no Brasil: abordagem data-driven
No full textThis work aims to create analyses and models from open public data made available by the Brazilian Federal Highway Police Department (DPRF) for public health intelligence and to develop an analytical dashboard for monitoring data and formulating hypotheses. One of the targets of the Sustainable Development Goals for Health and Well-being (SDG 3) is to “halve global deaths and injuries from road accidents by 2030”. Although fatal accidents have decreased from 2010 to 2020 and have been increasing since then, Brazil is the third largest country in deaths from traffic accidents, which makes the problem a public health issue. The results show that the main causes of fatalities are human error (illegal overtaking) in poor road conditions (single lane, rural area) or peak times (weekends and nighttime)
IT Identity fostering User Innovation on Digital Platforms: The Role of Self-Esteem
Get PDFDigital platforms can grow by motivating users to explore new ways to use a wider range of affiliated products and services. This work explores the power of IT Identity to motivate such innovative use, through identity\u27s ability to intrinsically motivate behavior. Data from 209 Amazon.com users indicates that IT Identity may cause Trying to Innovate with an IT, mediated by Self-Esteem
Understanding Communication Interdependencies in Corporate Sustainability Discourse
Get PDFThis study investigates the interdependent nature of corporate communication in sustainability discourse on social media. Using digital trace data from S&P 500 firms\u27 Twitter accounts (2015–2021), we examine how corporations engage in sustainability communication not in isolation, but through patterned and interconnected behaviors shaped by institutional dynamics. We employ the Generalized Connectedness Approach to quantify sector-level interdependencies, revealing strong cross-sector influence in sustainability posting behavior. Based on network position, environmental business orientation, and responsiveness to public attention, we identify four emergent groups of corporations. We then leverage algorithm-supported induction to elicit feature importance, revealing that patterns of sustainability posts, sociopolitical posting, ESG scores, and social media visibility are key predictors of corporate group (i.e., identified in the previous step) membership. We situate these findings within the theoretical lexicon of mimetic isomorphism and organizational routines, arguing that corporate sustainability discourse is shaped by routinized and mimetic communication practices
Retention vs. Excitement, A Dilemma in Protocological Control and Resistance in Esports Patching
Get PDFPatching in esports presents critical technological and managerial challenges, requiring developers to balance novelty, accessibility, and competitive integrity. Prior research has largely overlooked how patching strategies influence sustained player engagement. This study examines which types and complexities of patch changes best support long-term player retention in the esports ecosystem. Using Dota 2 as a case study, we categorized patches from 2012 to 2024 via ChatGPT-4o-mini and constructed time series data linking patch types to player counts. Applying Interrupted Time Series (ITS) and Generalized Least Squares (GLS) models, we evaluated short-term excitement and long-term retention effects, controlling for seasonality and autocorrelation. Findings reveal that while complex patches boost immediate engagement, they risk undermining long-term retention. Our study advances theory on digital ecosystem dynamics and offers practical insights for optimizing patch strategies in esports
The Impact of Metacognitive AI on Appropriate Reliance in AI-Assisted Decision-Making: The Role of Trust Resilience and Critical Thinking
Get PDFAs AI becomes increasingly integrated into high-stakes decision-making, ensuring appropriate reliance—users’ ability to calibrate their trust based on AI reliability—remains a critical challenge. Metacognitive AI, which monitors and regulates its own decision-making, has the potential to improve trust calibration by fostering trust resilience and critical thinking engagement. However, its enhanced self-reflective capabilities may also lead to over-reliance due to its perceived authority. Drawing on dual-process theory and obedience to authority theory, this study investigates how metacognitive AI influences user reliance behaviors. Using a between-subjects experimental design, 200 participants will interact with AI advisors exhibiting high or low metacognitive ability in a medical diagnosis task. We examine the effects on trust resilience, critical thinking, and reliance patterns, moderated by Need for Cognition (NFC). Findings will contribute to the design of AI systems that foster appropriate reliance and decision-making autonomy, reducing automation bias and improving human-AI collaboration
Le paradigme critique dans les travaux français en MSI : Trajectoire d’une institutionnalisation entre pluridisciplinarité et autonomisation (2014-2024)
No full textDepuis les années 1980, le paradigme critique en management des systèmes d’information (MSI) s’est structuré à l’international en réponse aux approches positivistes et techno-centrées. Dans la communauté française, son évolution demeure encore peu étudiée. Cette communication propose une analyse historique des recherches critiques en MSI à travers un corpus de 908 communications issues des conférences de l’AIM sur la période 2014-2024. L’étude révèle une transition en deux séquences : une première (2014-2020) marquée par une influence pluridisciplinaire et une seconde (2021-2024) caractérisée par un renforcement identitaire. L’essor récent des problématiques liées à l’IA semble marquer un tournant dans le développement des approches critiques, tout en s’inscrivant dans le domaine des questions éthiques du numérique. Face à ce constat, nos résultats soulignent la nécessité de structurer davantage ce paradigme afin de mieux répondre aux enjeux contemporains des SI et d’élargir la place des perspectives critiques au sein de la recherche francophone