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Evolution of Patient Perceptions and Experiences with Telehealth: Insights from Reddit Communities
No full textTelehealth has evolved over multiple decades in the US, with the COVID-19 pandemic acting as a catalyst for the removal of regulatory and reimbursement barriers, thereby accelerating its adoption. Despite this rapid growth, there remains a significant gap in understanding patients\u27 perceptions of telehealth, which is crucial for optimizing healthcare delivery and ensuring patient satisfaction. In this research, we investigate the evolution of patient perceptions toward telehealth over time and identify the factors influencing these changes through text mining. Additionally, we focus on patients with mental disorders, specifically those with anxiety, to understand how telehealth affects their healthcare experiences. Data is collected from Reddit, a highly popular social media platform where user posts are organized by subject into user-created boards called subreddits. We examine three subreddits: r/telemedicine, r/telehealth, and r/anxiety. This research provides valuable insights into the dynamic nature of patients\u27 perceptions of telehealth. Understanding how these perceptions change over time can inform strategies to enhance telehealth adoption, address barriers, and tailor services to meet evolving patient needs. By examining telehealth utilization, satisfaction, and preferences, healthcare providers and policymakers can better design interventions to sustain positive patient experiences and increase the acceptance of telehealth services
Antecedents to Cybersecurity Breach Value Erosion: Machine Learning Approaches
No full textAbstract Cybersecurity breaches impose both direct costs (statutory audit fees) and hidden costs (non‑audit fees for remediation, consulting, and reputation management). Yet, the organizational antecedents that drive these transaction costs are poorly understood. Adopting Transaction Cost Economics as our theoretical lens, we analyze 261 breach events across 147 publicly traded firms (2009–2020). We merge Compustat financials with data on global footprint (number of countries), regulatory intensity (NAICS classification), breach complexity (data types compromised), and attack vectors. Using a suite of explainable machine‑learning models (Decision Tree, Random Forest, Support Vector Regression, and XGBoost), we log‑transform audit and non‑audit fees to capture non‑linear effects and rank predictor importance. Our results reveal that global footprint is the strongest driver of increased audit fees, reflecting elevated monitoring costs when firms operate across multiple jurisdictions. Moderately regulated industries incur the highest combined transaction costs, suggesting that insufficient compliance infrastructures exacerbate both audit and remediation expenses. Breach complexity and sophisticated attack vectors predominantly elevate non‑audit fees, as firms invest heavily in consulting, legal counsel, and reputation repair. These findings offer actionable insights for practitioners: executives can tailor cybersecurity investments and incident‑response protocols based on organizational scope and industry profile, while policymakers may refine disclosure requirements to incentivize stronger pre‑breach controls. Methodologically, this study demonstrates the value of explainable AI in translating high‑dimensional breach data into strategic guidance
The Role of Sleep in Navigating Off-Work ICT Work Demands
No full textIn today’s constantly connected work environment, employees face increasing off-hour interruptions driven by information and communication technologies (ICT), which can deplete cognitive resources and contribute to emotional exhaustion. Drawing on Domain Switch Theory and Cognitive Load Theory, we examine how sleep functions not merely as a disrupted outcome of ICT demands but as a protective resource that helps employees manage the cumulative strain of off-hour interruptions. Using five-day multilevel data from working adults, we find that off-hour interruptions mediate the relationship between ICT availability demands and next-day emotional exhaustion, and that this indirect effect is significantly weaker among individuals who obtain more sleep. Our findings position sleep as a strategic resilience factor and offer theoretical and practical insights for supporting employee well-being in digitally demanding work settings
Digital Banking Systems Success Factors Model: Case of Ethiopian Banking Sector
No full textDigital Banking Systems is being one of the long practiced technological advancement in information systems around the Globe. However, it is in its early phase of implementing banking technologies in the case of Ethiopian Banking Sector due to infrastructural and IT human capital limitations. Advancements in digital technologies such as big data, Artificial Intelligence, Cloud Computing and Robotics drive the successful implementation of digital banking systems in the banking sector (Alalwan et al., 2020). However, as an Information Systems, digital banking systems investments if not very well managed in a way that can bring such intended benefits might be devastating especially for low-income countries such as Ethiopia because such investments does not guarantee successful implementation and desired return on investment. Accordingly, digital banking systems success factors model that can guide its successful implementation has paramount importance to maintain its successful implementation. The purpose of this research is then to investigate success factors of digital banking systems and then develop a model that can map the infrastructural constraints of banks in developing nations context by taking the case of the Ethiopian Banking sector
Teaching Digital Literacy Course using Spreadsheet in the Age of AI: Challenges and Opportunities
No full textCybersecurity Resilience of Telehealth Teams: The Effects of Cognitive Load and Security Fatigue
No full textLarge-scale unexpected events, such as the COVID-19 pandemic, have accelerated the virtualization of processes across many industries, including healthcare (Ayabakan et al., 2024). Due to arrangements such as social distancing and remote work life changes, telehealth and telemedicine-based services became more ubiquitous after the pandemic. Telehealth is the use of electronic information and telecommunication technologies to support long-distance clinical health care, patient and professional health-related education, health administration and public health (Adler-Milstein et al., 2014). Additionally, geographic healthcare disparity has been a long-standing global social problem, and telehealth has bridged barriers to improve health resource disparity without requiring physical relocation of healthcare providers. Despite the many advantages of telehealth expansion, these clinical systems require prioritization of cybersecurity strategies to prevent cyber-attacks and safeguard employee health information. Telehealth poses unique security risks unlike conventional healthcare, with teams operating across varied security environments, non-specialists managing multiple devices, and real-time data exchange requirements. Some of these attacks include ransomware, phishing, denial of service (DoS), malware, and password attacks, among many others. Healthcare data is highly sensitive; cyber breaches cause unquantifiable reputational, legal and financial damages. As a result, we posit that cybersecurity compliance of telehealth teams alone is not enough - we seek to examine their resilience. We define cybersecurity resilience as a team\u27s ability to anticipate, withstand, comply with, and recover from cyber threats, focusing on adaptability and continuity, such as restoring systems after a breach. We seek to examine the impact of human factors through the lens of cognitive load theory (Sweller et al., 2019) and the moderating role of security fatigue. We will use a survey approach to assess team cybersecurity resilience through intrinsic, extrinsic, and germane cognitive loads, moderated by security fatigue. For generalizability, we will include telehealth workers across all roles, including IT staff and clinicians. Data will be collected from telehealth teams across different regions to account for variability in cybersecurity practices. We will use structural equation modeling (SEM) to analyze the relationships between these factors. This research will contribute to the cybersecurity compliance, resilience, and telehealth adoption literature
Platform Approaches by and for Marginalized Entrepreneurs: Give Fish vs Lead to Water
No full textWe need to consider platforms for development, especially for marginalized populations (Ahuja et al., 2023; Bonina et al., 2021). Marginalized populations have been left out of recent advances in mitigating digital divide (Vassilakopoulou & Hustad, 2021), and even see marginalization increase (Heeks 2022). Entrepreneurial support organizations address the digital divide by providing resources to marginalized entrepreneurs. To make a broader impact on a large scale, these organizations use a variety of digital platforms and practices (Chan et al. 2022). Digital platforms connect people and services, and they can also facilitate innovation. In our observations, two broad approaches are used by entrepreneurial support organizations to empower marginalized entrepreneurs. The first approach, “Lead to Water” (L2W) helps entrepreneurs gain knowledge and information. The second approach, “Give Fish” (GF), helps entrepreneurs by creating spaces and tools for them to sell their products using digital platforms. The main difference between these two approaches is whether entrepreneurs are expected to learn the technology or whether they are provided with readily available technologies. Expecting that each approach has advantages, in this study we are focusing on the specific research question, “Which entrepreneurial support organization practices will help which entrepreneurs meet which needs and goals?” A secondary research question is “How does the development of those platforms unfold?” Cases will be chosen for their common antecedents (Eisenhardt, 2021), including all organizations studied focusing on the same population, and their contrasting approaches. Data will be gathered through interviews and ethnographic immersion in entrepreneurial support organizations. The goal is to understand practices followed by entrepreneurial support organizations to bridge the digital divide at scale to empower marginalized entrepreneurs
How Does the Stock Market View Firms\u27 AI-Related Initiatives?
No full textThe rapid advancement of artificial intelligence (AI) has led firms to integrate AI technologies into their strategic agendas, given AI’s transformative potential to boost productivity and reshape competition. Firms increasingly make public announcements about their AI initiatives to inform investors and stakeholders. Yet, how the stock market interprets and reacts to these announcements remains unclear, particularly amid uncertainty regarding AI’s implementation and outcomes. Unlike earlier IT that primarily improved operational efficiency, AI can augment or replace complex cognitive and decision-making tasks and enable adaptive, self-learning systems. These attributes raise AI’s strategic importance, meaning investor reactions may reflect a mix of innovation enthusiasm and concerns about risk, feasibility, and execution capability. Accordingly, this study examines how investors evaluate AI-related announcements by analyzing stock market reactions around these events. It seeks to determine whether such announcements signal firm-specific strategic value or suggest broader industry disruption, potentially affecting perceptions of both the announcing firm and its competitors. This study compares stock price movements before and after the announcements. Preliminary results indicate that the market does not respond uniformly to AI announcements. Instead, investor responses appear context-dependent, influenced by factors such as timing and competitive landscape. This research contributes to the digital innovation literature by positioning AI as a transformative force with the potential to disrupt industry norms. For practitioners, the findings underscore the importance of strategic communication and execution when launching AI initiatives
Ethical Considerations When Using LLMs
Full text linkArtificial intelligence (AI) is currently being used in a wide range of areas, and is not limited to automating repetitive tasks, but to improve learning, refine ideas, and optimize processes by enhancing human skills through AI’s ability to analyze large volumes of data and produce insights (Meira, 2025). As a result, evaluating the ethical implications of these advancements is increasingly essential to develop solutions that benefit all stakeholders and prevent unintended consequences from poorly designed or implemented technologies. Some risks are perpetuating biases and inequality, compromising privacy and safety, missing opportunities to shape the future, being unable to make informed decisions, and failing to understand the implications of AI on society (Todtfeld & Weinstein, 2025). For example, MIT recently made available an AI Risk Repository (Slattery et al., 2024). However, even with the repository, it is difficult to know which risks to worry about the most, as it requires a better understanding of how these systems work. A study by the World Economic Forum (Skeet et al., 2020) revealed that 66% of people worry that technology will make it impossible to know whether what they are seeing or hearing is real. Data privacy is highly valued by consumers, with more than half of respondents — 53% — saying they would avoid buying from a company if it sells personal data for profit. Therefore, behaving ethically definitely matters when creating sustainable value, and companies can create lasting value for society by aligning their practices with the needs of all stakeholders, including the community at large. Although there is a serious ethical risk, ethical problems in AI solutions often arise because well-intentioned people fail to design and use AI with an intentional ethical context in mind. Against failing to keep intentional ethical context in mind, we are addressing the following research questions: (1) How could a model based on principles support informed decision-making in AI/LLM use given AI’s impact on individuals and society? (2) How do ethical considerations for LLMs differ significantly from broader AI concerns? With these research questions, our proposed model centers on principles adapted from (Beard & Longstaff, 2018) and in the proposed framework from (da Silva et al, 2024), explaining how LLMs fit within this framework and what new insights our model contributes. Principles considered are Ought before can, Benefit, Responsibility, Non-instrumentalism, Purpose, and Fairness, evaluating their applicability in the context of LLMs. Our research also examines the technical decisions in data collection, attribute selection, and system design. The objective is to illustrate how to effectively design solutions with a clear focus on ethical considerations at every stage of the decision-making process
Mindful Coding: Student Agency and AI Partnership in Software Development Learning
Full text linkSoftware development has become an increasingly vital skill in the AI era and the demand for individuals proficient in software development continues to grow. However, Generative AI (GenAI) tools such as ChatGPT and GitHub Copilot are transforming software development education. This study examines the transformative role of GenAI in software development education, highlighting both its educational benefits and associated risks. Initially met with scepticism due to concerns around academic integrity (Daun & Brings, 2023), GenAI is now increasingly viewed as a valuable educational tool that can deliver personalised instruction, generate diverse learning resources, and enhance student engagement (Sengul, Neykova, & Destefanis, 2024; Sun, Boudouaia, Zhu, & Li, 2024). This ongoing study follows a qualitative approach using two data sources: Reddit discussions and interviews. Over 15,000 comments were collected from several subreddits using the Reddit API, targeting conversations about AI in learning software programming. In addition, 16 semi-structured interviews were conducted with computer science academics across multiple countries. Topic modelling (Hannigan et al., 2019) was applied to Reddit data, and thematic analysis was used to code the interview data. Topic modelling produced five categories, including “Leveraging Custom AI Agents” and “Balancing Innovation and IP Protection”. Interview analysis revealed three main themes. AI as a Learning Partner and Personal Mentor: interviews revealed that AI tools are not merely utilities but collaborative agents that coach software development students, scaffold codes and trigger self-reflection by students. Students negotiate how far to lean on AI while developing cognition about its benefits and limits. Future-Proofing Software-Development Education: Faculties are re-engineering curricula, practices, and assessments so graduates master enduring principles of computing and software engineering as well as the AI-mediated workflows that industry now expects. Authenticity and integrity safeguards are essential to keep learning meaningful. Developing Responsible and Ethical AI Fluency: Beyond technical skills, students and prospective programmers aim to build a critical ethical lens in spotting bias, protecting privacy, and using AI responsibly. This fluency is recognised as a graduate attribute and a pre-empt against shallow, AI-driven shortcuts. The next phase of our research will integrate Reddit discourse and academic perspectives to develop a robust framework for the responsible use of GenAI in software programming education