AIS Electronic Library (AISeL)
Not a member yet
72426 research outputs found
Sort by
Driving AI Adoption - The Role of AI Managers as Boundary Spanners
No full textAs artificial intelligence (AI) transitions from an experimental technology to a strategic priority, organizations seek to align their AI initiatives with business objectives. However, AI adoption frequently fails due to a lack of internal expertise, unclear responsibilities and a lack of attention to organizational, regulatory and work design factors. In this context, we observe the emergence of dedicated AI roles driving AI adoption—referred to as AI Managers. Despite their emergence, the role of AI Managers remains under-researched. Through a qualitative interview study, we examine how AI Managers leverage boundary spanning mechanisms—boundary objects and boundary activities—to drive AI adoption. Our results indicate that the success of AI adoption depends on the alignment of these mechanisms to foster interdisciplinary collaboration, structured AI governance and business value creation. This study contributes by extending the boundary spanning theory to the AI context, highlighting the institutionalization of AI Managers as boundary spanners
AI-Driven Decentralized IoT for Secure and Scalable Healthcare
No full textAI Innovations in the IoT for Real-Time Patient Monitoring On one hand, the current traditional centralized healthcare architecture poses numerous issues, including data privacy, delay, and security. Here, we present an AI-enabled decentralized IoT architecture to address such challenges during a pandemic and critical care settings. This work presents our architecture to enhance the effectiveness of the current available federated learning, blockchain, and edge computing approach, maximizing data privacy, minimizing latency, and improving other general system metrics. Experimental results demonstrate transaction latency, energy consumption, and data throughput orders of magnitude lower than competitive cloud solutions
Nurturing Sustainable Digital Health through Actors Interaction and Value Co-creation: Towards a Digital Health Business Model
No full textStakeholder interaction and value co-creation are critical in creating a sustainable digital health ecosystem. While digital health is widely recognized for its potential to transform healthcare, previous studies tend to neglect the interactions among actors involved in digital health projects and give even less emphasis to how those interactions contribute to value co-creation in sustainable ways. Furthermore, existing research predominantly focuses on developed economies where digital infrastructure, healthcare systems, and technology adoption are more mature. Consequently, the findings from previous studies, while valuable, are not entirely applicable to developing economies where challenges such as limited digital infrastructure, resource constraints, low digital literacy, and fragmented healthcare systems prevail. This research-in-progress paper aims to explore this issue and attempts to propose a noble conceptual research framework on how a sustainable digital health business model is developed through social exchange theory (SET), Service-Dominant (S-D) Logic, and Triple Bottom Line (TBL) theories
Exploring Self-Sovereign Identity Solutions for Improved Identity and Access Management in the European Union
No full textSelf-sovereign identity is an emerging digital concept that grants individuals greater control over their data. The implementation and regulation of SSI in the European Union face challenges due to the diverse policies and legal regulations, including the General Data Protection Regulation and eIDAS. This systematic literature review allows us to appreciate the extent to which SSI can potentially enhance identity access management while identifying the benefits, challenges, and limitations associated with its implementation and adoption within the EU’s legal framework. By synthesizing existing research, this work seeks to inform both IS literature in four key areas (technical, legal, policy and behavioral) as well as practice
Preventing Double Spending of Time in Remote Agency Work
No full textRemote work is increasingly becoming a significant part of the economy, transforming the work dynamic between employers and employees into more of an agency relationship. The growing gig economy further intensifies agency risks, especially moral hazard due to information asymmetry. One such issue is the double spending of time, where an agent submits invoices to multiple and unrelated principals for the same time period—effectively a form of contract cheating. This introduces risks of IP loss and sub-optimal solutions. We enhance the common agent model to incorporate the time domain and describe conditions under which contract cheating can occur or improve the Nash equilibrium. We then propose a blockchain-based algorithmic solution—PauliChain—to address this problem. Our approach adapts double-spending prevention in cryptocurrency to remote knowledge work, allowing principals to verify exclusive time allocation across distributed work environments
Understanding Human Learning Performance through AI Mentor Interactions
No full textThis study investigated how synthetic voice characteristics influence human learning in AI-mentored environments. This study uses acoustic theory and dual-stream cognitive models to examine two key voice dimensions: voice production (conventional vs. personalized synthetic voices) and voice transmission (radiation vs. conduction). Through a controlled laboratory experiment involving a maze-based experiential learning task in Minecraft, the study finds that personalized synthetic voices enhance learning when transmitted through radiation but hinder learning when delivered via conduction. These effects are explained by processing consistency, that is, how well the mode of delivery aligns with the users’ cognitive processing style. Radiation fosters deliberative thinking, benefiting from external-sounding voices, whereas conduction evokes self-talk, which may lead to misattribution and fast, shallow processing. This research extends information systems and human-computer interaction literature by showing how acoustic voice features shape cognition, engagement, and performance in AI-assisted learning
Shortcuts or Blind Spots? The Impact of Search Generative Experience (SGE) on Information-Searching
Full text linkThis study investigates how Google\u27s Search Generative Experience (SGE), an innovative hybrid search model integrating generative AI summaries with traditional search, impacts user information-searching behaviors. Building on Information Foraging Theory and Cognitive Load Theory, the novel Adaptive Problem Space Construction (APSC) framework conceptualizes how AI-generated content reshapes users\u27 query refinement, cognitive effort, and problem-solving strategies. Utilizing the Type-Aloud Method, a video-based, non-intrusive approach that captures detailed user interactions, this research examines whether SGE promotes deeper cognitive engagement or leads to premature closure by encouraging users to accept synthesized information without further exploration. Results will provide theoretical insights into cognitive adaptation processes in AI-integrated environments and offer empirical evidence on search behaviors influenced by generative AI. Practically, this research informs real-time digital feedback mechanisms, supporting the strategic integration of GenAI and large language models in digital workplaces to enhance innovation, employee performance, and strategic decision-making
Algorithmic Accountability of Low-Code/No-Code Artificial Intelligence: A Literature Review
Full text linkAlgorithmic Accountability of Low-Code/No-Code Artificial Intelligence (LCNC AI) presents a significant challenge, as these platforms democratize AI development while diminishing direct oversight. Unlike traditional AI systems, applications built with LCNC AI tools often lack governance structures, increasing risks of bias, opacity, and regulatory non-compliance. Organizations struggle to implement accountability mechanisms as non-technical users deploy AI without comprehensive validation frameworks. This study conducts a Structured Literature Review (SLR) to analyze existing research on algorithmic accountability in LCNC AI. The findings highlight critical risks, governance approaches, and accountability frameworks essential for mitigating ethical and compliance concerns. The study emphasizes the necessity of hybrid governance approaches, integrating organizational oversight with user-driven compliance measures. To bridge research gaps, this study proposes a research agenda aimed at refining ethical and regulatory frameworks for LCNC AI. By providing concrete governance strategies, this study offers practical recommendations for organizations to ensure accountable and responsible LCNC AI deployment
Revolutionizing Alzheimer’s Diagnosis: A Hybrid Deep Learning Approach for Enhanced MRI Analysis
Full text linkAlzheimer’s Disease (AD) is a neurodegenerative disorder that primarily affects the elderly, causing cognitive decline and memory loss. Traditional diagnostic methods, such as neuropsychological tests and cerebrospinal fluid analysis, are invasive and time-consuming. Neuroimaging techniques like MRI and PET provide valuable insights but require manual analysis by specialists. This study proposes a hybrid model combining EfficientNetB0, a deep learning architecture, with Convolutional Neural Networks (CNN) to automate AD detection in MRI scans. The model uses data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, which includes over 200 MRI scans and clinical information. Our results show that the hybrid model outperforms existing methods in accuracy and efficiency, detecting key AD pathology features such as amyloid beta plaques and neurofibrillary tangles. This work demonstrates the potential of AI-driven approaches for AD diagnosis, offering a more accessible, cost-effective solution for clinical settings with limited resources. Future research should explore multimodal integration and model interpretability. Keywords: Alzheimer\u27s Disease, Deep Learning, MRI, EfficientNetB0, Convolutional Neural Networks
Harnessing Hierarchical Clustering in Salience-Driven Text Summarization
Full text linkText summarization is critical in modern information management, enabling organizations to extract valuable information from vast text data. However, current summarization approaches face several limitations, such as inadequate interpretability, generating factually inconsistent content, and heavy dependence on large language models (LLMs). These limitations present obstacles in environments where efficiency and reliability are paramount, or resources are constrained. To address these challenges, we introduce DocuSage, an interpretable text summarization framework designed to overcome the limitations of current methods. DocuSage combines novel sentence extraction techniques with LLM-based abstraction to reduce hallucinations, maintain context, and reduce computational overhead while mimicking human summarization through hierarchical clustering and tree structures. Empirical tests on news articles show that their summaries closely align with human judgment and outperform baseline models. By using LLMs primarily to enhance coherence, DocuSage enables the deployment of a smaller, cost-effective model, offering a scalable, transparent, and adaptable solution for organizational information systems