AIS Electronic Library (AISeL)
Not a member yet
72426 research outputs found
Sort by
Cybersecurity 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
Get PDFArtificial 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
Get PDFSoftware 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
Teaching Data Preparation to Non-technical Audiences Using Tableau Prep Builder
Get PDFAn increasing number of Information Systems courses aim to improve students\u27 data analytics skills. Teaching such skills requires teaching data preparation practices. These skills were traditionally taught using tools targeted at technically trained audiences with coding abilities. However, there is an increasing demand to learn data preparation skills from non-technical audiences in business school programs. To this end, Tableau-Prep Builder (TPB) was used to teach Extract Transform Load at an introduction to analytics master’s level course in a business school program at the Université du Québec à Montréal in winter 2025. We note that TPB belongs to the same publisher as Tableau Desktop, the popular data visualization tool
Understanding Vaccine Awareness and Digital Engagement among African American Parents: A Survey-Based Approach
Get PDFAfrican American children continue to experience lower childhood vaccination rates (Immunizations and Black/African Americans | Office of Minority Health, 2025), driven by factors such as vaccine hesitancy, historical mistrust, and misinformation. Despite the availability of vaccines, rates of vaccination remain below recommended levels, particularly in underserved communities (Hill, 2024). This research-in-progress aims to explore the underlying awareness gaps and digital behaviors that may influence how African American parents, and future parents, make vaccine related decisions for their children. Research has shown that digital health interventions are effective tools in increasing health knowledge and engagement, especially amongst ethnic and cultural minorities (Radu et al., 2023). We employ a survey-based methodology to assess baseline knowledge of vaccine schedules, perceptions of vaccine safety, and openness to digital tools for health education. The survey also captures digital literacy, mobile app usage patterns, and attitudes toward gamification in health contexts. Early findings are expected to provide a detailed understanding of informational needs, behavioral barriers, and the potential acceptance and utilization of technological solutions. This study is part of a broader research agenda to design inclusive and culturally relevant digital health interventions. By grounding our future app development in data from impacted communities, we aim to improve the effectiveness and trustworthiness of public health messaging. Improved public health messaging is especially important for populations that exhibit distrust of health institutions due to historical harm. Our preliminary results will provide insights into how African American parents and future parents perceive vaccination content and strategies to improve digital engagement in childhood healthcare. Preliminary results will also help inform design choices for future mobile health tools aimed at broader populations
Beyond the Crowd: A Literature Review of AI’s Impact on Crowdsourcing Systems
Get PDFCrowdsourcing harnesses the collective intelligence of diverse individuals across organizational boundaries. Traditionally, job posters define the task requirements, post jobs on crowdsourcing platforms, and assess the quality of the work. Recent advancements in artificial intelligence have begun to streamline the entire process. Artificial intelligence can assist job posters in clearly defining job requirements, distributing tasks to qualified workers by matching job requirements with their backgrounds, and evaluating the quality of contributions. The advantage of AI in crowdsourcing is its compatibility with various crowdsourcing models (e.g., idea generation) (Dissanayake et al., 2025). In the idea generation model, AI can group and highlight similar generated ideas as submissions roll in and evaluate their quality based on novelty and feasibility in achieving the goals. Similarly, AI can detect abnormalities (e.g., speedy response times) in the microtasking model and calculate the task error rate. Scholars in information systems (IS) and other disciplines have investigated the role of AI in various fields, demonstrating its potential in handling complex tasks. While current research has explored the use of AI in crowdsourcing, the findings are scattered and vary among crowdsourcing models. The literature review of AI in crowdsourcing provides a clear understanding of how AI transforms crowdsourcing operations and identifies research gaps. Therefore, we organize and analyze the current use of AI in crowdsourcing operations and its impact on these operations. Our work employs the Input-Process-Output (IPO) model, a framework widely used in management research, to examine the current application of AI in crowdsourcing. This framework allows us to decompose the crowdsourcing process into three subprocesses: input, process, and output (Ghezzi et al., 2017). The “input” stage involves defining tasks that workers should perform. Building on this foundation, the “process” stage focuses on how job posters manage the crowdsourcing session (e.g., organizing the submissions during the session), which is necessary for the “output” stage, where solutions are evaluated and selected. In a crowdsourcing process, AI plays a unique role, and how and where AI is used in the process determines the outcomes for each stage (e.g., enhanced clarity of job descriptions). Understanding the outcomes of using AI in crowdsourcing can help us assess the impact of AI on the crowdsourcing process. Therefore, we propose the following research questions: “How is AI applied across the stages of a crowdsourcing process among various crowdsourcing models? ” (RQ1) and “What are the impacts of AI usage across the stages of the crowdsourcing process among various crowdsourcing models? ” (RQ2). In conclusion, this literature review offers a clear understanding of the current application of AI in the crowdsourcing process by breaking down the process into three subprocesses, enabling an examination of the nuances of AI\u27s impact on each subprocess
Novel model for Better Segmentation
Get PDFDigital imaging techniques have advanced significantly since the 1960s (Gonzalez & Woods, 2007), with computer algorithms being employed to enhance contrast, encode intensity levels, and enable efficient object recognition. These advancements have revolutionized various fields such as X-ray interpretation, medical image analysis, and satellite imaging. Image segmentation, as a critical preprocessing step, is essential for tasks ranging from precise disease diagnosis (e.g., tumor localization in CT scans) to environmental monitoring (e.g., land cover classification in satellite imagery). The state-of-the-art models for image segmentation often leverage information from multiple scales, with the U-Net architecture being one of the most prominent examples (Szegedy et al., 2015). U-Net’s distinctive U-shaped architecture utilizes skip connections to merge high-level semantic feature maps from the decoder with corresponding low-level detailed feature maps from the encoder (Smith & Doe, 2022). Combined with powerful data augmentation techniques, U-Net maximizes the use of limited annotated samples. However, traditional segmentation methods often struggle with complex feature extraction and computational efficiency, especially in scenarios with limited annotated data or resource-constrained environments. To address these challenges, attention mechanisms have emerged as a powerful tool to enhance model sensitivity to task-relevant features. Among them, the Efficient Channel Attention (ECA) mechanism stands out due to its ability to adaptively recalibrate channel-wise feature responses without dimensionality reduction, significantly reducing computational overhead while maintaining performance Prior studies have demonstrated the effectiveness of ECA-integrated architectures in medical imaging. For instance, ECAU-Net improved fetal ultrasound cerebellum segmentation (Brahmankar et al., 2022) and enhanced performance in coronary artery segmentation and three-dimensional reconstruction (Brahmankar et al., 2022). Yet, these applications remain confined to the medical domain, with limited exploration in non-medical contexts. Building upon the success of U-Net in brain tumor image segmentation (Doe & Smith, 2024) and cerebellum segmentation for clinical diagnosis (Murugan & Karuppiah, 2022), we are among the first study to introduces the first extension of ECA-enhanced U-Net architecture to general image segmentation tasks beyond healthcare. We term this approach ECAU-Net, which integrates the Efficient Channel Attention (ECA) mechanism into U-Net’s skip connections to dynamically prioritizes informative channels across scales, enabling robust segmentation in diverse scenarios such as industrial defect inspection and agricultural crop monitoring, while preserving computational efficiency. By applying the encoder-decoder architecture, ECAU-Net efficiently locates segmentation results, making it a powerful backbone for various segmentation applications. As a result, the improved U-Net demonstrates a significant enhancement in segmentation accuracy. During the experimental evaluation, the improved model was trained and systematically assessed, with results showing a consistent 2% improvement in key metrics—mean intersection over union (mIoU), mean pixel accuracy (mPA), precision, and recall—compared to the traditional U-Net and faster convergence in training loss. These improvements underscore the efficacy of the proposed approach, demonstrating that the addition of the ECA attention mechanism leads to more precise and reliable segmentation outcomes
AI and Higher Education: Navigating the New Frontier
Get PDFWith increasingly powerful generative artificial intelligence (AI) tools now widely available, post-secondary institutions are struggling to keep up with a rapidly evolving landscape. While much has been discussed about the potential impact of this technology, there is still limited empirical data on how students, educators, and administrators are integrating AI in teaching and learning contexts. In this presentation, we share key findings from a multi-methods study conducted by The Conference Board of Canada, on behalf of the Future Skills Centre (FSC). The study includes a national survey of postsecondary students (N=2,401) and educators (N=402), as well as interviews with individuals leading responses to AI in higher education institutions (N=42). We found that frequent usage was not widespread among students, with 20 per cent of students reporting using generative AI most or all of the time. Usage varied significantly across students in different sociodemographic groups and fields of study. Power users – those who report using generative AI most or all of the time – had similar levels of concern as non-users about the potential drawbacks of generative AI, despite having more favourable attitudes toward its use. We also found an association between frequency of use and better learning experiences and outcomes, but the mechanisms and conditions under which this occurs need to be further investigated. Among educators, we found that most have neither explicitly permitted nor prohibited student use of AI tools. Notably, 80% reported not receiving any formal guidance or training from their institutions. There is a strong demand for professional development in this area, with educators seeking training for both themselves and their students. Educators who use generative AI more frequently tend to be more optimistic about its potential, although they remain wary of its ethical implications and possible threats to the integrity and reliability of knowledge. Conversations with institutional leaders revealed a wide range of perspectives on AI, from views that it has radically transformed the role of the teacher, to skepticism about its overall impact. Many leaders expressed enthusiasm for AI as a tool for enhancing higher-order learning. These findings have important implications for various post-secondary stakeholders, particularly instructors and administrators looking to integrate AI into educational environments. We conclude with recommendations focused on fostering critical literacy, ensuring transparency and accountability in AI use, and promoting equity in access to AI tools and training