With the growing emphasis on global digitalization strategies, and in the context of both the diverse needs of a super-aged society and the shortage of nursing personnel, this study integrated the Resource-Based View (RBV) and the Unified Theory of Acceptance and Use of Technology (UTAUT) models to examine how internal resources and capabilities of long-term care institutions affect digital maturity. It also analyzed the factors influencing professionals' intention to adopt digital technologies. Within the framework of digital maturity, behavioral intention, and digital transformation application, digital maturity is identified as the most critical component. The results indicated that technology has a significant impact on digital maturity, while cultural and organizational factors do not. Personal innovativeness and performance expectancy significantly affect behavioral intention, whereas effort expectancy and social influence show no significant effect. The empirical analysis validated the key factors influencing digital transformation, providing managerial implications for leaders in formulating digital transformation strategies, and establishing a foundation for how long-term care institutions can leverage digital technologies to enhance service quality and efficiency.
Curriculum Design in competency-based education often lacks means to capture relationships between competencies and courses intended to develop them. In this paper, we present ForestED an interactive visualization tool which addresses this issue. Applied to curricula, ForestED displays visualizations of the curricula that render explicit the links between competencies and courses. While the visualization reflects the static curriculum definition, it also allows inference of competency development over time through the course timeline.
The deployment of driverless vehicles in urban environments raises concerns about pedestrian safety due to the loss of traditional driver communication cues. This study investigated the subjective crossing experience of young and older adult pedestrians facing two different driverless vehicles in a shared space. In a virtual reality setting, 44 participants (24 young adults and 20 older adults) were asked to cross while a driverless car and a driverless shuttle approached. The complexity of the crossing was manipulated so that the two driverless vehicles either had the same behavior (i.e., both yielding or both passing) or different behaviors (i.e., one yielding, the other passing). Additionally, the two vehicles could both be equipped with an external Human-Machine Interface (eHMI) indicating their respective intention (i.e., to yield or to pass) using visual and sound signals, or had none. After each crossing, the participants rated their perceived safety and understanding of the vehicles' intentions using questionnaires. Semi-structured interviews were conducted post-experiment to gather qualitative feedback on the participants' crossings and the bimodal eHMI. Firstly, our results indicated that the older adults reported better understanding of both the cars and shuttle's intentions than the young adults, likely due to their broader integration of environmental cues. Moreover, a learning effect among the older adults was found, indicating improved understanding of the car's intentions over time when the two vehicles exhibited different behaviors, reflecting preserved learning abilities in normal ageing that support adaptation to complex traffic scenarios. Furthermore, both age groups reported an initial loss of perceived safety when the two vehicles behave differently, which diminished with repeated exposure, suggesting an adaptive learning process in complex traffic scenarios. Finally, the presence of the bimodal eHMI on driverless vehicles demonstrated a positive impact at different levels of the pedestrian crossing experience. These findings are further discussed.
The point cloud part segmentation task consists of segmenting an object, represented by a point cloud, into its constituent parts, such as a chair that is segmented into seat, backrest, and legs. The most recent computational strategies use Artificial Neural Networks to perform this task, but the architectures used are developed generically and therefore do not consider the specific patterns of each category of objects. Thus, this work proposes to analyze the contribution of building specific architectures based on the optimization of hyperparameters of the PointNet architecture, which is well established in the literature. The dataset used was the PartNet, and four case studies were employed. In addition, we also studied the impact of point cloud size on this segmentation task, performing the optimization process in each category studied in three different point cloud sizes: 512, 1,024, and 2,048. From the results obtained, an average improvement of 2% in the test accuracy metric was achieved in the Table-1, Chair-1, and Lamp-1 categories and 6% in the StorageFurniture-1 category. The impact of point cloud size was low, and statistically significant improvements were observed in Table-1, Chair-1, and StorageFurniture-1 categories. Thus, hyperparameter optimization proved to be consistent, achieving satisfactory results.
Background: Stress-related disorders and chronic diseases are increasing globally, highlighting the need for integrative health indices that go beyond isolated physiological signals. Objective: We introduce the Physiological Wellness Index (PWI), a wearable-compatible composite that integrates autonomic, respiratory, and electrodermal domains into a single interpretable score for real-time wellness monitoring. Methods: The PWI synthesizes heart rate variability (HRV, proxied through PRV RMSSD), respiratory rate (RR, as the operational proxy for Breathing Efficacy), and electrodermal activity (EDA) through a weighted normalization framework. Each signal is scaled per participant, with RR and EDA inverted so that higher component values consistently reflect better physiology. The index outputs a 0-100 score, mapped into three actionable categories: restful (70-100), active (40-69), and distressed (0-39). The validation was performed on the ZU-PWD '25 dataset, comprising 28 adults, 15 months of monitoring, heterogeneous data coverage, the PWI reliably distinguished baseline from stress days and tracked acute episodes characterized by EDA spikes, suppressed HRV, and elevated RR. The composite index classified data with greater stability and offered trajectories that were more easily interpreted compared to single-signal measures. Conclusions: The PWI represents a transparent, sensor-driven alternative to traditional fitness and wellbeing metrics. By addressing raw multimodal physiology with actionable states, it supports preventive healthcare, workplace and educational wellness, and clinical decision-making, marking a significant step forward on the IoMT landscape.
Modern software systems have become so complex that explaining their decisions poses significant challenges for both developers and users. This article addresses the explainability of component-based Knowledge Graph Question Answering (KGQA) systems, where components often rely on AI-driven processes. Such processes can be opaque, making it difficult even for KGQA experts to interpret the underlying behavior and outcomes. To tackle this issue, we propose an approach that leverages the input and output data flows of system components as a basis for representing their behavior and generating explanations. This enables users to better understand how decisions are made. In the KGQA framework considered here, component data flows are expressed as SPARQL queries (inputs) and RDF triples (outputs). Consequently, our work also provides insights into verbalizing these data types. Through experiments, we evaluate our approach, comparing template-based explanation generation (baseline) with automatic generation using Large Language Models (LLMs) configured in various ways. The results demonstrate that LLM-generated explanations are of high quality and generally outperform template-based methods based on user evaluations. This approach thus facilitates the automated natural-language explanation of KGQA components' behavior and decisions, contextualized within RDF and SPARQL representations.
In an era characterised by increasing cyber threats, there is a pressing need to gain a deeper understanding of how to enhance digital resilience at the individual level. In this study, we compared the role of actual knowledge with individuals' perceived knowledge, and the influence on the intentions to engage in self-protective behaviour. Participants (N = 222) completed an online questionnaire, which included an actual knowledge test focusing on protection measures and items measuring constructs of Protection Motivation Theory. Our regression analyses showed that actual knowledge is a predictor to engage in self-protective behaviour, with a stronger influence than perceived knowledge. Both actual and perceived knowledge are vital for self-protective behaviour, however, they activate different types of efficacy. Actual knowledge is a stronger predictor of response efficacy, whereas perceived knowledge better predicts self-efficacy. We explored implications for intervention and future research in the discussion, by highlighting the need for prioritising knowledge dissemination, increasing user confidence, and the effectiveness of interventions. This paper is a substantial adaptation of a previous conference paper (Bluhm et al., 2025).
This study investigates how bias, cooperation, and power asymmetries unfold between minority and majority groups in immersive social virtual reality (SVR). Seventy-two Jewish Israelis-including Ethiopian Israelis, ultra-Orthodox (Haredi), and secular participants-took part in structured collaborative and competitive sessions within a shared virtual gallery, and their behaviors were analyzed through qualitative observation and post-session questionnaires. Five interrelated themes emerged: spatial dynamics of partner selection, whereby initial choices were driven by immediate virtual proximity rather than cultural identity; cultural identity signaling in VR, as verbal references and customized avatar elements (e.g., head coverings and color palettes) subtly reinforced group belonging; a digital divide in VR interactions, with technological proficiency functioning as social capital that enabled skilled users to assume leadership roles; gender roles in virtual interactions, where explicit gender cues embedded in avatars saw women more often mediating or supporting and men directing task flow; and trust building through virtual interaction, progressing from competence-based partner selection to cross-group coalitions and an emotional shift from discomfort to connection. Together, these findings show that while SVR mirrors real-world power structures and identity cues, its immersive, task-oriented design can also catalyze new cross-group bonds, highlighting the need to narrow technological gaps and encourage inclusive collaboration to realize SVR's potential as a bridge across social divides.
In this paper, we outline a general methodology for analysing corporate career data using data extracted from professional CVs. Our process begins by collecting resumes in JSON format automatically and storing them in a centralized repository. These data are then systematically structured into both relational and graph databases, enabling rich, multidimensional analysis. Through the integration of database management techniques and machine learning tools, we can conduct further analysis, such as sequence analysis, to trace and interpret career paths, uncovering career patterns of business elites. This framework not only facilitates detailed empirical investigation but is also designed to be reproducible and adaptable, serving as a foundation for future research.
A new paradigm in information technology and enterprise computing, fog computing allows organisations to significantly boost efficiency and productivity. It is essential in developing countries where resource efficiency is key. However, research is scarce in Saudi Arabia regarding fog computing adoption, especially identifying key influencing factors, benefits, and challenges. To bridge this gap, this study examines the determinants of fog computing adoption in Saudi Arabian public organisations through a mixed-method research approach. Initially, semi-structured interviews were conducted with 15 IT managers to gain qualitative insights. From these discussions, complexity was identified as the only significant barrier to adoption. Subsequently, a large-scale quantitative analysis was carried out, involving 665 IT managers and employees. This phase validated the proposed framework and revealed that, out of 12 identified factors, privacy, complexity, awareness, and senior management support were not significantly associated with adoption intention. By integrating qualitative and quantitative findings, this research highlights the crucial role played by technical, organisational, environmental, and financial factors in influencing fog computing adoption. These insights provide IT managers in Saudi public sector organisations with a comprehensive and strategic understanding, enabling them to make well-informed decisions regarding its effective implementation. With a structured and holistic approach, organisations can maximise the benefits of fog computing while effectively addressing potential challenges, ensuring its successful adoption in the evolving technological landscape.
In today's ubiquitous computing environment, organizations rely heavily on Information Technology (IT) to drive business value and economic success. However, true organizational sustainability extends beyond economics to include environmental and social dimensions, particularly in how IT equipment is disposed of, how software is developed, how data is generated and managed, how digital infrastructure is powered, and how end-user behaviour influences energy consumption. Energy-intensive data centers and growing data volumes contribute significantly to carbon emissions, necessitating greener IT practices. This study explores key knowledge management (KM) strategies and tactics that support green IT initiatives to promote organizational sustainability. Based on 539 survey responses, factor analysis revealed three core KM strategies: performance tracking and measurement, dark data management, and knowledge exchange. A multiple-factor analysis further examined the perspectives of KM practitioners, organizational learning practitioners, and data analysts, uncovering a notable divergence between the KM and learning communities versus data professionals. The results are synthesized into a KM strategies and tactics work system designed to guide organizations in aligning KM efforts with sustainability objectives by fostering interdisciplinary engagement and collaboration.
Digital technologies have become deeply embedded within the present computing environments that shape users' daily interactions, emerging as essential tools for education, communication, and information exchange. While users generally engage with these technologies through digital interactions, individuals with visual impairments (VI) or visual disabilities (VD) encounter distinct challenges compared to their sighted counterparts. In the specific context of computer-aided drawing systems for the blind (CADB), existing scholarship has significantly advanced our understanding of assistive technologies and the unique ways in which blind and visually impaired (BVI) users interact with human-computer interfaces. It is crucial to enhance usability, support shape creation, and enable intuitive spatial navigation to fully leverage the potential of these interfaces and foster meaningful engagement. This study sought to identify key user experience (UX) principles relevant to the design of screen readers-critical tools in improving digital accessibility for BVI users. The research analyzed a curated selection of 18 scholarly works employing a systematic literature review methodology. A thematic analysis of these sources produced 86 sub-themes, organized into seven overarching themes: Navigation, Content Creation Tools, Interactive Technology, UX Design Principles, Evolving Technology, User Needs, and User Skills. These themes were synthesized into a conceptual model offering actionable insights for UX designers and developers. The resulting framework not only informs the development of more accessible and user-centered screen readers but also contributes to the broader goal of inclusive digital design by ensuring that assistive technologies address the diverse needs of their users.
This paper explores the impact of digital transformation on organizational culture, employee resilience, and business operations, analyzing the attitudes of managers and employees. Digital transformation represents a key challenge for organizations, as it not only changes the technological aspects of business operations but also shapes the way organizations work, communicate, and interact with each other. The focus of this research is on how organizational culture evolves in the context of digitalization and how employees develop resilience to changes caused by new technologies and business practices. Through an analysis of the attitudes of managers and employees, the paper explores how different groups perceive organizational changes, the challenges of adapting to new technologies, and the impact of these changes on the work environment, productivity, and innovation. It also emphasizes how employee resilience can be key to successfully managing transformation, maintaining high performance, and preserving organizational culture in times of rapid technological change. The research results indicate the importance of actively involving all stakeholders in the digital transformation process, as well as the need to develop strategies that support employee resilience and preserve a positive organizational culture, which is crucial for long-term competitiveness and business success.
