Vulnerable Road Users (VRUs) face the highest risk of road accidents. Various innovations aim to enhance their safety, including the development of Automated Vehicles (AVs). As AVs get integrated into traffic, VRUs might interact with them at crossings. To ensure efficient development of AV, it is crucial to understand how VRUs adapt their behaviour and make road-crossing decisions infront of AVs. This paper is part of a longitudinal Virtual Reality (VR) experiment where participants crossed a road under varying environmental conditions. Interviews were carried out using self-confrontation technique, where participants watched a video of their road-crossing, then reflected on their decisions. This method helps explain quantitative data but rarely used in pedestrian-AV studies. Inductive content analysis was used to analyse the transcripts, categorising findings into two dimensions. The findings reveal that participants' decision-making varied across the scenarios and experimental groups. The two general dimensions- human-computer interaction and user experience can guide the design of a framework to understand pedestrian-AV interactions. The analysis provided deeper understanding of pedestrians' crossing behaviour, highlighting factors influencing decisions and behavioural adaptation. Gaps in AV communication were identified and design recommendations for signalling, emphasising the need for intuitive, pedestrian-friendly AV behaviour to enhance safety was provided.
Context has been invoked as an explanatory concept in philosophy, psychology, and most recently in cognitive systems engineering and computer science. An analysis of how context has been defined suggests that context or its effects are manifest in many ways. Examples of contextualising - the 'discovery' of context and its implications - point to a definition of context, one that goes beyond the notion of context as 'just more information.' The article considers the implications of a model of contextualising for ergonomic theory and the design of human-AI work systems.
To uncover a link between prolonged sitting and low back disorders, this study assessed how lumbar spine passive stiffness varied with activities and seated spine kinematics. Data were collected from twenty participants performing seated office work throughout a work week. The time spent sitting, standing/stepping, and moderate-vigorous physical activity (MPVA) were derived from a thigh-worn activity monitor. Thorax inclination and lumbar spine flexion and movement were calculated from low back tri-axial accelerometers. Lumbar spine flexion passive stiffness was measured on Monday morning and evening, Tuesday morning, Friday evening, and the following Monday morning. Forward-backward subset regression models were constructed between spine stiffness and activities and seated kinematics, then models were subjected to bootstrapping. The median adjusted R-2 ranged from 0.10 to 0.65 for stiffness measures in low to moderate flexion ranges. Sitting time, particularly at work, was associated with earlier tissue engagement and reductions in the low stiffness zone slope, while MVPA demonstrated the opposite effects. More frequent and smaller spine micromovements while seated were associated with smaller changes in passive stiffness. Modifying workplace sitting behaviours and incorporating movement, through MVPA and in sitting, may help mitigate the mechanical changes in the spine that could contribute to low back disorders.
Distracted driving remains a significant contributor to traffic accidents because attentional capacity is often diverted from driving to secondary tasks (e.g. thinking or talking), which increases the risk of human error. This study examined the effectiveness of visual warning stimuli in guiding driver attention during distractions. Twelve university students completed simulated driving tasks under three conditions: normal driving (control), and driving with concurrent 2-back and 3-back auditory tasks to vary distraction level. We measured eye movements to determine the eye movement anomaly score, which was calculated as the Mahalanobis distance from the normal driving state. We then evaluated the probability of a decrease in the anomaly score after warning presentation. The results showed that the anomaly score decreased with a probability significantly greater than 50% after visual warning presentation in control and moderate-load (2-back) conditions. However, under high cognitive load (3-back), visual warnings were less effective in reducing the anomaly score. These findings suggest that adaptive visual warning systems that account for drivers' real-time cognitive state may help modulate gaze-related attentional state, and indicate the need for further research into optimal warning thresholds and multimodal interventions.
Microsoft Copilot is an AI-based assistant designed to help users perform various tasks. However, the adoption and effective use of such systems are related to usability and user experience. Human-Computer Interaction (HCI) developments aim to make AI systems more intuitive, accessible and user-friendly. In this study, the usability of Microsoft Copilot was examined in terms of effectiveness and efficiency, and the relationship between usability satisfaction and AI acceptance was analysed. The study was conducted with 20 voluntary university students, using quantitative and qualitative methods. Participants completed tasks of varying difficulty using Copilot and completed a satisfaction survey and an AI acceptance scale. The findings indicate that Copilot is generally user-friendly and that users exhibit high levels of satisfaction. Additionally, a strong and significant relationship was found between usability satisfaction and the AI acceptance dimensions of effort expectancy, performance expectancy and facilitating conditions. Users found Copilot's content creative, considered its helpful functionality and assessed its interface as accessible. However, some improvements were deemed necessary, including enhancing motivation, ensuring operational consistency and addressing data security concerns. In this context, it is recommended that Copilot focuses on performance-enhancing improvements and security measures to enhance the user experience further.
Job well-being is of utmost importance for individuals and organisations, yet various constraints hinder managers and employees, including unfavourable policies, inappropriate practices, and workplace environment. Workplace bullying, a significant negative event, profoundly impacts organisational workforce regardless of its nature. This study investigates workplace bullying's effect on job well-being among banking sector employees in Pakistan, examining the moderating role of perceived organisational support and the mediating role of emotional exhaustion. Data from 420 bank employees in selected Pakistani cities underwent analysis using Smart-PLS. Findings indicate that workplace bullying has detrimental consequences for job well-being among banking employees in Pakistan, leading to increased emotional exhaustion. Perceived organisational support emerges as a crucial moderator, alleviating the adverse effects of bullying on job well-being. The study highlights the importance of addressing workplace bullying within the banking industry to enhance employee well-being and organisational health. Perceived organisational support plays a pivotal role in mitigating the negative impact of workplace bullying on job well-being among banking employees in Pakistan, emphasising the need for organisational interventions to foster a supportive work environment.
Modern socio-technical systems demand management initiatives that acknowledge work as dynamic and negotiated, rather than fixedly prescribed. This paper discusses the benefits of pattern finding (inductive detection of weak signals) and pattern priming (deductive use of a Work-as-X archetype catalogue) to achieve a larger understanding of work via dedicated patterns. The notion of work system pattern is first formalised with first-order logic and then positioned within the Structured Exploration of Complex Adaptations (SECA) method, designed as a dual-loop engine for its operationalisation towards weak signals identification. This pattern-based analytical inquiry is meant to accelerate sense-making, support organisational learning, and sharpen resilience potentials, especially in volatile high-risk socio-technical system.
Virtual Reality (VR) has the potential to provide qualitative social interactions. The literature explores the Social Presence and Co-presence notions, which we propose to pursue with the definition of the Koinos concept, to name the perceived quality of social interactions in a mediated environment. This definition derives from the Predictive Coding Theory and Qualia Theory, which takes into account an individual's own subjective feelings. In a second time, we propose the Koinos model, which represents the dynamics of social interactions in VR, and can be used as a tool to predict the Koinos generated by a specific VR configuration. The model reflects the mediated message transmitted during social interactions in VR, taking into account the common ground between interlocutors.
Icons are widely used in health communication because complex information can be conveyed rapidly and with minimal cognitive demand. The purpose of this study was to analyse the effects of icon design formats on matching performance across multiple preventive functions related to COVID-19. A total of 105 standardised pictograms representing 21 preventive functions were developed in five formats (image-related, concept-related, semi-abstract, word, and combined) and designed in accordance with ISO 9186-1:2014 to ensure visual consistency. Thirty adults completed a computer-based matching task in which each pictogram was linked to its intended function, with matching accuracy and response time recorded. One-way ANOVA showed that icon format significantly influenced matching accuracy but not matching time, whereas preventive function significantly affected both measures. Word and combined formats demonstrated the highest accuracy, followed by image-related and semi-abstract formats, while concept-related icons performed poorest. Confusion matrix analysis of icons with accuracy below 75% revealed systematic misclassifications caused by overlapping visual features and semantic similarity, particularly among closely related respiratory function. Design guidelines derived from the confusion patterns emphasise prioritising combined formats for confusable functions and avoiding concept-related formats for preventive behaviours. Overall, the findings provide an ergonomics-informed foundation for evidence-based redesign of safety-critical health pictograms.
Information behaviour research has evolved from a traditional focus on information sources to a user-centric perspective. While Cognitive Work Analysis (CWA) is a well-established framework for analysing complex sociotechnical systems in domains like process control and aviation, its application remains nascent within the specific context of library and information science, particularly for modelling endogenous user search behaviour. This paper posits a novel theoretical integration, arguing that CWA's constraint-based, work-centred framework provides a uniquely powerful lens to move beyond descriptive models of information seeking. We propose a refined, user-centred application of CWA to dissect the complex interplay between the library environment, organisational structures, user tasks, and cognitive strategies. This approach offers a novel methodological pathway to generate design requirements for information systems that are not merely usable but are cognitively congruent, supporting the adaptive expertise of library users. By bridging CWA's systemic rigour with the nuanced realities of information behaviour, this paper aims to advance both theoretical discourse and practical design paradigms within information science.
The SHELL model has remained one of the most widely used conceptual tools in Human Factors since its introduction, shaping training, safety research, and system analysis across aviation, medicine, and defence. Its representation of human interaction within complex systems has been influential; however, the model's architecture is predominantly external, focusing on the relationships between Software, Hardware, Environment, and Liveware components, while neglecting the intrapersonal dimension that regulates these interactions. Over the past two decades, interdisciplinary research in psychology, cognitive science, and neuroscience has highlighted the critical role of self-consciousness in performance regulation, resilience, and error prevention, indicating a limitation in the explanatory and predictive capacity of the traditional SHELL framework. In response, this paper employs a conceptual synthesis and cross-sectoral analytical methodology to examine the limitations of existing Human Factors models and proposes an extension, SSHELL, which integrates Self-consciousness as a new central element. SSHELL reframes the human operator as a self-regulating, conscious system that actively manages both internal states and external system interactions. By reintroducing the self into system thinking, the SSHELL framework advances Human Factors theory by offering a domain-neutral model applicable across aviation, healthcare, military, and educational systems, while highlighting the need for future empirical validation.
Within the framework of Conservation of Resources theory, this study explores the psycho-behavioural pathway linking algorithmic dehumanisation to the safety behaviours of ride-hailing drivers (RHDs). We position ego depletion as the mediating mechanism and mindfulness as a potential moderator. Data from a two-wave survey of 209 Pakistani RHDs, analysed with PROCESS Macro, reveal that algorithmic dehumanisation significantly erodes self-regulatory resources, resulting in ego depletion and, consequently, diminished safety compliance and participation. Although mindfulness buffers the experience of ego depletion, it does not directly restore safety performance. This finding underscores a critical gap: individual resilience strategies are insufficient to offset systemic risks embedded in algorithmic control. The study contributes to occupational safety and gig-economy literature by recasting algorithmic dehumanisation as a tangible job demand and risk factor. We conclude with imperative practical recommendations for ride-hailing platforms, including algorithmic transparency, contextual performance metrics, and integrative well-being interventions.
