Search Results (60)

The increasing complexity of fleet operations often forces drivers and administrators to alternate between fragmented tools for geolocation, messaging, and spreadsheet-based reporting, which slows response times and increases cognitive load. This study evaluates a comprehensive architectural framework designed to automate fleet management in personnel transport companies. The research proposes a unified methodology integrating Internet-of-Things (IoT) telemetry, cloud analytics, and Conversational AI to mitigate information fragmentation. Through a Lean UX iterative process, the proposed system was modeled and validated, with 30 participants (10 administrators and 20 drivers) who performed representative operational tasks in a simulated environment. Usability was assessed through the System Usability Scale (SUS), obtaining a score of 71.5 out of 100, classified as “Good Usability”. The results demonstrate that combining conversational interfaces with centralized operational data reduces friction, accelerates decision-making, and improves the overall user experience in fleet management contexts. Full article

This study presents DeepVisionAnalytics, an integrated framework that combines eye tracking, OpenCV-based computer vision (CV), and machine learning (ML) to support objective analysis of consumer behaviour in visually driven tasks. Unlike conventional self-reported surveys, which are prone to cognitive bias, recall errors, and social desirability effects, the proposed approach relies on direct behavioural measurements of visual attention. The system captures gaze distribution and fixation dynamics during interaction with products or interfaces. It uses AOI-level eye tracking metrics as the sole behavioural signal to infer candidate choice under constrained experimental conditions. In parallel, OpenCV and ML perform facial analysis to estimate demographic attributes (age, gender, and ethnicity). These attributes are collected independently and linked post hoc to gaze-derived outcomes. Demographics are not used as predictive features for choice inference. Instead, they are used as contextual metadata to support stratified, segment-level interpretation. Empirical results show that gaze-based inference closely reproduces observed choice distributions in short-horizon, visually driven tasks. Demographic estimates enable meaningful post hoc segmentation without affecting the decision mechanism. Together, these results show that multimodal integration can move beyond descriptive heatmaps. The platform produces reproducible decision-support artefacts, including AOI rankings, heatmaps, and segment-level summaries, grounded in objective behavioural data. By separating the decision signal (gaze) from contextual descriptors (demographics), this work contributes a reusable end-to-end platform for marketing and UX research. It supports choice inference under constrained conditions and segment-level interpretation without demographic priors in the decision mechanism. Full article

Digital cultural heritage platforms face a dual challenge: preserving rich historical information while engaging an audience with declining attention spans. This paper addresses that challenge by proposing a modular information architecture designed to mitigate cognitive overload in cultural heritage tourism applications. We begin by examining evidence of diminishing sustained attention in digital user experience and its specific ramifications for cultural heritage sites, where dense content can overwhelm users. Grounded in cognitive load theory and principles of user-centered design, we outline a theoretical framework linking mental models, findability, and modular information architecture. We then present a user-centric modeling methodology that elicits visitor mental models and tasks (via card sorting, contextual inquiry, etc.), informing the specification of content components and semantic metadata (leveraging standards like Dublin Core and CIDOC-CRM). A visual framework is introduced that maps user tasks to content components, clusters these into UI components with progressive disclosure, and adapts them into screen instances suited to context, illustrated through a step-by-step walkthrough. Using this framework, we comparatively evaluate personalization and information structuring strategies in three platforms—TripAdvisor, Google Arts and Culture, and Airbnb Experiences—against criteria of cognitive load mitigation and user engagement. We also discuss how this modular architecture provides a structural foundation for human-centered, explainable AI–driven personalization and recommender services in cultural heritage contexts. The analysis reveals gaps in current designs (e.g., overwhelming content or passive user roles) and highlights best practices (such as tailored recommendations and progressive reveal of details). We conclude with implications for designing cultural heritage experiences that are cognitively accessible yet richly informative, summarizing contributions and suggesting future research in cultural UX, component-based design, and adaptive content delivery. Full article

The global rise in childhood speech disorders highlights the need for accessible and engaging home-based rehabilitation tools. This study applied the Double Diamond design framework to enhance the user experience (UX) of Smart Speech, a gamified functional speech therapy program. Using heuristic evaluation, expert interviews, and benchmarking, six core UX problem areas were identified, including insufficient guidance, low personalized motivation, limited feedback, and accessibility issues. Through an iterative ideation process, 78 UX improvement concepts were generated, encompassing motivational reinforcement (e.g., praise stickers and character interaction), automated training guidance, enhanced feedback mechanisms, and error-prevention features. A usability evaluation with 20 participants, including speech-language pathologists (SLPs) and parents, showed significant improvements across key dimensions, with increases of 1.1 to 2.6 points on a 7-point scale. These findings demonstrate that systematic UX design can substantially improve engagement, usability, and the potential therapeutic utility of home-based speech therapy systems. Full article

Requirements Engineering and Management (REM) tools play a significant role in ensuring project compliance and efficiency. Automotive engineering must comply with regulatory standards, requiring detailed documentation, rigorous testing, and solid traceability. Despite their importance, REM tools are underexplored from the usability and user experience perspective (UX), even though poor usability can hinder development workflows across stakeholder teams. This study presents a case study of heuristic usability evaluation of IBM DOORS Next Generation, conducted with expert evaluators, using Nielsen’s 10 Usability Heuristics as an evaluation framework. The identified issues were analyzed in terms of impacted heuristics and severity ratings. Additionally, we underwent a Large Language Model (LLM)-based heuristic evaluation, using ChatGPT-5, prompted with the same heuristic set and static screenshots. The LLM detected several issues overlapping with human findings (32%), as well as new ones (23%); therefore, 55% of its outputs are considered valid and 45% are unconfirmed. This highlights both the potential and limitations of AI-driven usability assessment. Overall, the findings underscore the usability challenges of REM tools and suggest that LLMs may serve as complementary evaluators, accelerating early-stage heuristic inspections in safety-critical engineering environments. Full article

Spatial Augmented Reality (SAR) has evolved in the past fifteen years from a whimsical, projection-based approach to a socially nuanced medium of interpretative scholarship for culture, education, and storytelling. This paper presents a critical literature review on SAR systems and cases in arts and culture, based on 52 papers selected over the last decade. The perspective of the review is that of interaction design, which is concerned in general with the practice of designing interactive digital products, environments, systems, and services, and in particular with how the specific characteristics of a physical space, the interaction modality, and the narrative impact the design and efficacy of SAR in art and heritage contexts. This paper reports on the technology landscape, the physical contexts and scales of deployment, interaction modalities, audiences, and evaluation methods of SAR in arts and culture. Then, we present our reflections on the current state-of-the-art in terms of sketching out a historic trajectory of the field, SAR-oriented narrative design patterns, issues of inclusion and accessibility, and several design tensions, constraints, and recommendations for interaction design. Finally, we discuss potential further work in several dimensions of designing SAR for arts and culture, and we present a research agenda. Full article

The electrification of transport is vital to achieving global climate targets, with electric vehicles (EVs) positioned as a sustainable alternative to fossil fuel–based mobility. However, the scalability of EV adoption hinges on the accessibility, reliability, and user experience of public charging infrastructure. As China leads the world in EV adoption, Shanghai represents a critical case for evaluating user satisfaction in a megacity context where infrastructure density, urban planning, and consumer behavior intersect. Despite significant investments in expanding charging facilities, limited empirical research has examined how users perceive and interact with Shanghai’s public EV charging network. This study addresses that gap through a quantitative, user-centered analysis of responses from 197 EV users using the QUESS-PAC framework (Quantitative User Experience Survey Strategy for Public EV Charging Analysis in Cities). A structured questionnaire assessed satisfaction across multiple dimensions: infrastructure layout, convenience, pricing, ease of use, safety, and lighting. Using SPSS (v28), descriptive analysis and multiple regression were conducted to identify key determinants of satisfaction. The findings indicate low overall user satisfaction, with critical weaknesses in location planning, cost transparency, and interface usability. Regression analysis highlights four significant predictors of satisfaction—layout, ease of use, pricing, and lighting—with charging price emerging as the most influential factor. This study’s unique contribution lies in the development and application of the QUESS-PAC framework, which integrates quantitative UX metrics with behavioral and spatial dimensions to provide a more systematic assessment than prior descriptive studies. It emphasizes the need for integrated planning that combines spatial equity, service design, and behavioral insights. Based on the analysis, policy recommendations are proposed to enhance satisfaction and encourage adoption. These findings offer transferable insights for global cities navigating the electrification of transport. Full article

Background: A key barrier to realizing the full potential and long-term collection of patient-reported outcomes (PROs) is the limited understanding of user experience (UX) factors that influence sustained patient engagement with digital PRO tools. Most existing research focuses on disease-specific or country-specific solutions, leaving a gap in identifying shared UX determinants that could inform scalable, cross-disease European digital health frameworks. This fragmentation hinders interoperability and increases development costs by requiring separate tools for each context. This case study aims to address this gap by identifying key UX features that optimize PRO collection across diverse chronic conditions in Europe within the Health Outcomes Observatory project, enhancing continuous (primary use) and large-scale (secondary use) data collection. Objective: This study aimed to identify and analyze key UX factors that support adoption and sustained use of PRO collection tools among patients with chronic diseases across multiple European countries. Methods: Patient focus groups were conducted in four chronic disease areas: cancer, inflammatory bowel disease (IBD), and diabetes (type I and II) across six European countries. Participants were recruited purposively through national patient advisory boards to ensure diversity in age, gender, and disease type. Sessions were moderated by trained qualitative researchers following a standardized guide, and discussions were transcribed verbatim and coded in researcher pairs to ensure intercoder reliability through iterative consensus. A modified thematic analysis, guided deductively by the UX Honeycomb model and inductively by emergent themes, was used to identify cross-disease UX determinants. Results: In total, 17 patients and patient representatives participated (76% female; 4 diabetes, 6 IBD and 7 cancer). We identified six core UX factors driving patient engagement for all disease groups: compatibility with other technologies, direct communication with the care team, personalization, ability to share data, the need for educational material and data protection were identified as key aspects of PRO technologies. However, the customizability of the app is crucial. Not all disease groups had the same needs, and participants specifically requested that the app provide information relevant to their own condition. Disease-specific needs, like T1D patients desiring glucose monitoring integration, were identified. IBD patients highlighted flare detection abilities and cancer patients especially sought side-effect comparisons. Conclusions: Our findings indicate that a unified yet customizable PRO platform can address shared UX needs across diseases, improving patient engagement and data quality. Incorporating features such as seamless data transfer, personalization, feedback, and strong privacy measures can foster trust and long-term adoption across European contexts. In addition to some disease-specific issues, most needs for the backbone of the app were shared among the disease areas. This shows that a shared app between diseases might be preferable and, in case of comorbidities, could ease self-management for patients. Last, to ensure full potential for every user and every disease, customization is crucial. Full article

With the ongoing integration of advanced technologies into modern vehicle systems, understanding user interaction becomes a critical factor for safe and intuitive operation—especially in the transition towards autonomous driving. This article uncovers user-reported challenges of UX and in-vehicle UIs. The analysis is based on quantitative and qualitative evaluations of user-generated content (UGC) from automotive-focused online forums. The quantitative analysis is conducted by Natural Language Processing (NLP), while qualitative evaluation is performed through Mayring, applying a deductive–inductive category formation approach. The study investigates challenges related to interface complexity, driver distraction, and missing user diversity in the context of increasing digitalization. Based on the analysis, a set of practical design implications is presented, emphasizing context-sensitive function reduction, multimodal interface concepts, and UX strategies for reducing complexity. It has become evident that UX concepts in the automotive context can only succeed if they are adaptive, safety-oriented, and tailored to the needs of heterogeneous user groups. This leads to the development of an interaction strategy model, serving as a transitional framework for guiding the shift from manual to fully automated driving scenarios. The paper concludes with an outlook on further research to validate and refine the implications and UX framework. Full article

Understanding and improving web performance is essential for enhancing user experience, yet existing approaches remain largely correlation-based and lack causal interpretability. To address this limitation, we propose a causal-inference-driven framework for diagnosing and optimizing user-centric Web Vitals such as Largest Contentful Paint (LCP), First Input Delay (FID), and Cumulative Layout Shift (CLS). Our contributions are threefold. (1) We construct a comprehensive feature representation that captures Document Object Model (DOM) structure, resource loading behaviors, rendering characteristics, and JavaScript execution, integrating browser-level domain knowledge into the modeling pipeline. (2) We introduce a hybrid causal discovery method that combines constraint-based reasoning with differentiable score-based learning to estimate high-dimensional causal structures reflecting real rendering processes. (3) We develop a causal-effect-based intervention optimization module that leverages counterfactual reasoning to identify actionable modifications for performance improvement. Our framework further leverages structural symmetries inherent in rendering processes, using repeated layout patterns and invariant dependency flows to reduce redundancy and strengthen the stability and identifiability of causal discovery. Extensive experiments on HTTP Archive, Chrome UX Report (CrUX), and a synthetic ground truth dataset demonstrate that our framework achieves higher causal accuracy, more stable predictive performance, more effective intervention recommendations, and improved interpretability compared with existing rule-based, statistical, and machine learning baselines. These results highlight the potential of causality-aware analysis for practical web performance optimization. Full article

Online customer reviews contain rich signals about product value but are difficult to convert into strategy-ready evidence. This study proposes an end-to-end framework that maps review text to the Value Proposition Canvas (VPC) and quantifies alignment between user needs and product performance. Using customer reviews for three Samsung Galaxy Watch generations, an LLM extracts six dimensions (Customer Jobs, Pains, Gains, Feature Gaps, Emotions, Usage Context). Extracted phrases are embedded with a transformer model, clustered via K-means with data-driven k selection, and labeled by an LLM to form an interpretable taxonomy. Subsequently, the analysis derives frequency profiles, a gap density indicator, a context–gap matrix, and a composite Product–Market Fit (PMF) score that balances gain rate, gap rate, and coverage with sensitivity analysis to alternative weights. The findings show predominantly positive affect, with unmet needs concentrated in battery endurance and interaction stability. Productivity- and interaction-centric jobs attain the highest PMF score, while several monitoring-centric jobs are comparatively weaker. Significant cross-generation differences in job composition indicate evolving usage priorities across successive releases. The framework provides a scalable, reproducible path from unstructured VOC to decision support, enabling data-driven prioritization for product and UX management while advancing theory-grounded analysis of customer value. Full article

Reliable schedule-risk estimation in hybrid software development lifecycles is strategically important for organizations adopting AI in software engineering. This study addresses that need by transforming routine process telemetry (CI/CD, SAST, traceability) into explainable, quantitative predictions of completion time and rework. This paper introduces an integrated probabilistic model of the hybrid software development lifecycle that combines Generalized Evaluation and Review Technique (GERT) network semantics with I-AND synchronization, explicit artificial-intelligence (AI) interventions, and a fuzzy treatment of epistemic uncertainty. The model embeds two controllable AI nodes–an AI Requirements Assistant and AI-augmented static code analysis, directly into the process topology and applies an analytical reduction to a W-function to obtain iteration-time distributions and release-success probabilities without resorting solely to simulation. Epistemic uncertainty on critical arcs is represented by fuzzy intervals and propagated via Zadeh’s extension principle, while aleatory variability is captured through stochastic branching. Parameter calibration relies on process telemetry (requirements traceability, static-analysis signals, continuous integration/continuous delivery, CI/CD, and history). A validation case (“system design → UX prototyping → implementation → quality assurance → deployment”) demonstrates practical use: large samples of process trajectories are generated under identical initial conditions and fixed random seeds, and kernel density estimation with Silverman’s bandwidth is applied to normalized histograms of continuous outcomes. Results indicate earlier defect detection, fewer late rework loops, thinner right tails of global duration, and an approximately threefold reduction in the expected number of rework cycles when AI is enabled. The framework yields interpretable, scenario-ready metrics for tuning quality-gate policies and automation levels in Agile/DevOps settings. Full article

Driver monitoring systems (DMS), advanced driver assistance ssystems (ADAs), and technologies for autonomous driving, along with other upcoming innovations, have been developed as possible solutions to minimize accidents resulting from human error. This paper presents a thorough review of DMSs and user experience (UX). The objective is to investigate, combine, and evaluate the key elements involved in the development and application of DMSs, as well as the UX factors relevant to the current landscape of the field, serving as a reference for future investigations. The review encompasses a bibliographic analysis performed at different stages, offering valuable insights into the evolution of the topic. It examines the processes of development and implementation of driver monitoring systems. Furthermore, this work facilitates future research by consolidating and presenting a valuable collection of identified datasets, both public and private, for various research purposes. From this evaluation, critical components for DMSs can be identified, establishing a foundation for future research by providing a framework for the adoption and integration of these systems. Full article

Water utilities are increasingly turning to digital solutions to promote conservation behaviours among households; however, traditional campaigns often suffer from limited personalisation, low interactivity, and modest long-term impact. Though computer-tailored and recommender systems (RSs) may offer personalisation, these systems lack a generalised framework that integrates behavioural theory with system design. This study addresses this research gap by introducing a novel framework that unites behavioural science, user experience (UX) design, and adaptive digital feedback to foster water-conscious practices at the residential level. The model draws on established behavioural theories, including the Theory of Planned Behaviour, the Transtheoretical Model, and Intervention Mapping, to ensure that tailored recommendations align with users’ psychological drivers, behavioural readiness, and daily routines. An industry-first prototype RS was developed and evaluated through an online survey (N = 300), assessing user perceptions of relevance, motivation, ease of use, and likelihood of action. The results reveal strong support for personalised suggestions, with 82% of respondents agreeing that personalised recommendations would help conserve water, and 76% indicating incentives would motivate adoption. This evidence indicates early acceptance and high potential impact. This study also addresses a critical research gap: no generic model previously existed to guide the integration of RSs with behaviour change interventions in water demand management. Broader implications are also discussed for applying the model to other sustainability domains such as energy use, waste reduction, and climate adaptation. Full article

Background/Objective: The global acceleration of population aging presents profound challenges to the physical, psychological, and social well-being of older adults. As traditional exercise programs face limitations in accessibility, personalization, and sustained social support, there is a critical need for innovative, inclusive, and community-integrated digital movement solutions. This study aimed to develop and evaluate Movement Poomasi, a hybrid digital healthcare application designed to promote physical activity, improve digital accessibility, and strengthen social connectedness among older adults. Methods: From March 2023 to November 2023, Movement Poomasi was developed through an iterative user-centered design process involving domain experts in physical therapy and sports psychology. In this study, the term UI/UX—short for user interface and user experience—refers to the overall design and interaction framework of the application, encompassing visual layout, navigation flow, accessibility features, and user engagement optimization tailored to older adults’ sensory, cognitive, and motor characteristics. The application integrates adaptive exercise modules, senior-optimized UI/UX, voice-assisted navigation, and peer-interaction features to enable both home-based and in-person movement engagement. A two-phase usability validation was conducted. A 4-week pilot test with 15 older adults assessed the prototype, followed by a formal 6-week study with 50 participants (≥65 years), stratified by digital literacy and activity background. Quantitative metrics—movement completion rates, session duration, and engagement with social features—were analyzed alongside semi-structured interviews. Statistical analysis included ANOVA and regression to examine usability and engagement outcomes. The application has continued iterative testing and refinement until May 2025, and it is scheduled for re-launch under the name Wello! in August 2025. Results: Post-implementation UI refinements significantly increased navigation success rates (from 68% to 87%, p = 0.042). ANOVA revealed that movement selection and peer-interaction tasks posed greater cognitive load (p < 0.01). A strong positive correlation was found between digital literacy and task performance (r = 0.68, p < 0.05). Weekly participation increased by 38%, with 81% of participants reporting enhanced social connectedness through group challenges and hybrid peer-led meetups. Despite high satisfaction scores (mean 4.6 ± 0.4), usability challenges remained among low-literacy users, indicating the need for further interface simplification. Conclusions: The findings underscore the potential of hybrid digital platforms tailored to older adults’ physical, cognitive, and social needs. Movement Poomasi demonstrates scalable feasibility and contributes to reducing the digital divide while fostering active aging. Future directions include AI-assisted onboarding, adaptive tutorials, and expanded integration with community care ecosystems to enhance long-term engagement and inclusivity. Full article