Trust, risk perception, and intention to use autonomous vehicles:an interdisciplinary bibliometric review

Autonomous vehicles (AV) offer promising benefits to society in terms of safety, environmental impact and increased mobility. However, acute challenges persist with any novel technology, inlcuding the perceived risks and trust underlying public acceptance. While research examining the current state of AV public perceptions and future challenges related to both societal and individual barriers to trust and risk perceptions is emerging, it is highly fragmented across disciplines. To address this research gap, by using the Web of Science database, our study undertakes a bibliometric and performance analysis to identify the conceptual and intellectual structures of trust and risk narratives within the AV research field by investigating engineering, social sciences, marketing, and business and infrastructure domains to offer an interdisciplinary approach. Our analysis provides an overview of the key research area across the search categories of ‘trust’ and ‘risk’. Our results show three main clusters with regard to trust and risk, namely, behavioural aspects of AV interaction; uptake and acceptance; and modelling human–automation interaction. The synthesis of the literature allows a better understanding of the public perception of AV and its historical conception and development. It further offers a robust model of public perception in AV, outlining the key themes found in the literature and, in turn, offers critical directions for future research.</p

Assessing the contribution of local journalism:the local newspaper as accidental social infrastructure

The value of local journalism is a pressing question for society because of the challenge posed to established business models by digital platforms. However, while local journalism is understood to be of benefit to people, discussion of the nature of those benefits and the ways in which they are accrued is dominated by a comparatively narrow focus on its outputs. Using the case of the legacy commercial local newspaper, this paper argues that local journalism as a process, practice and presence can be considered part of the accidental social infrastructure—part of the fabric which underpins strong communities, even if its intended purpose is something else. Data drawn from interviews with people working with local newspaper archives demonstrates how local journalism facilitates the development of social capital and processes of sociality. It demonstrates an expanded conceptual lens to articulate its benefits

Designing sustainable supply chain metrics for the Indonesian fashion industry:A DEMATEL-Based ANP approach

The development of the fashion industry, while providing significant impact to the economy, also has negative environmental and social consequences. Thus, many countries, including Indonesia, require the fashion industry to adopt sustainable supply chain management. To ensure the success of their implementation, it is necessary to determine the key indicators that might influence the implementation of a sustainable supply chain, especially in the context of the Indonesian fashion industry. This study aims to determine key performance indicators (KPIs) in implementing sustainable supply chain management for the Indonesian fashion industry. This study utilised a Decision-Making Trial and Evaluation Laboratory based Analytical Network Process (DEMATEL-based ANP) method to analyse and prioritise the indicators for three sustainability dimensions. This study reveals several variables for assessing the sustainable supply chain performance, which include operating costs, customer satisfaction, environmental compliance, implementation of environmentally friendly technology, and education and training. The outcomes of this study could be further used by the fashion industry to assess current performance and discover opportunities to improve its positive impact on society

Pricing Models for IoT and AI-Enhanced Sensor Monitoring Apps

The paper presents the development of a pricing strategy for a company that develops a sensor monitoring app leveraging IoT and AI technologies to enhance operational efficiency and connectivity. In alignment with the company's client objectives of predicting failures and improving operational engagement, the app's development followed a structured process. This process encompassed a Customer Value Proposition Workshop, Scenario and Affordance Analyses, Client Validation, Features Analysis, and Pricing Strategy. Through these steps, critical customer needs were identified and integrated into the app's features, supported by iterative feedback from the company's engineering department. Affordance theory was adopted as the theoretical lens and was proven instrumental in identifying how IoT can achieve operational goals by guiding the app's feature development. The pricing strategy employed value-based bundling and psychological pricing to enhance market appeal. This research highlights the transformative potential of IoT and AI in engineering consultancy, driving tailored solutions and operational excellence

Predicting Next Useful Location With Context-Awareness: The State-Of-The-Art

Predicting the future location of mobile objects reinforces location-aware services with proactive intelligence and helps businesses and decision-makers with better planning and near real-time scheduling in different applications such as traffic congestion control, location-aware advertisements, and monitoring public health and well-being. Recent developments in smartphone and location sensors technology and the prevalence of using location-based social networks alongside the improvements in artificial intelligence and machine learning techniques provide an excellent opportunity to exploit massive amounts of historical and real-time contextual information to recognise mobility patterns and achieve more accurate and intelligent predictions. This unique survey provides a comprehensive overview of the next useful location prediction problem with context-awareness and the related studies. First, we explain the concepts of context and context-awareness and define the next location prediction problem. Then we analyse more than thirty studies in this field concerning the prediction method, the challenges addressed, the datasets and metrics used for training and evaluating the model, and the types of context incorporated. Finally, we discuss the advantages and disadvantages of different approaches, focusing on the usefulness of the predicted location and identifying the open challenges and future work on this subject

Moralities colliding in crisis: the moral orientations of organisational habitus

This paper is based on multi-sited ethnographic investigation into a community development initiative called Big Local. Residents in deprived areas of England were invited to form community groups to take control of the funding awarded to their neighbourhood, and to make decisions about how that funding should be spent. This paper shows that group members brought with them different «organisational habitus» (Shoshan 2018): developed dispositions about how to organise that had been acquired through previous involvement in collective organising. Rather than focus solely on the practices of collective organising, however, I propose that these organisational habitus were anchored by two different «moral orientations»: one steeped in a sense of responsibility to include, the other to govern resources effectively. My objective is to show that the practices of organisational habitus cannot be isolated from the moral orientations that anchor them. In doing so, the paper shows that morality is not only fundamental to individuals’ motivations for engaging in collective action; why they get involved and what they hope to achieve, but also to the very practice of organising. The analysis illustrates the entanglement of sense and practice, showing how one’s motivation to participate shapes how one goes about doing so. This is both theoretically significant, in illustrating that practices of organising are not merely technical but morally imbued, while also having practical implications, by generating understanding of potential sources of tension, cohesion and longevity in groups. This suggests that those leading and facilitating civil society organisations would do well to facilitate conversations about how community groups choose to work, the way they do, and why. Doing so could help unearth members’ positions about the change they want to bring about, overcoming tensions in groups, and cultivating an empowered civil society consciously working towards its imagined «ideal society» (Lichterman &amp; Eliasoph 2014)

‘Heartbreak Gives Way to Hope After Grieving’:A Critical Analysis of Personal Stories of Pregnancy Loss in the UK Press

Pregnancy loss continues to occupy an uncomfortable space in public discourse. This article analyses articles in the UK press published between 2013 and 2023 referencing the Baby Loss Awareness Week (BLAW) that focus on narratives of personal experience of pregnancy loss. These narratives sit between the personal and the public, and constitute examples of public grieving that serve a range of purposes, from the expression of continuing bonds with the miscarried or stillborn baby, the desire to share one's experience of loss, and often to the wish to translate this experience into something positive. We draw on Finch's concept of ‘displaying families’, considering how they construct an identity for the baby who never lived and demonstrate continuing bonds with them. By analysing how this ‘identity work’ is made public, we explore how pregnancy loss is presented in public media as a transformative teleological experience for parents

A systematic scoping review of multidisciplinary teamworking in surgical services:the need for bariatric surgery research

This review aims to identify and map the extent and nature of published research investigating multidisciplinary teamworking in surgical services and evaluate the relevance of the evidence base to bariatric surgery. A systematic search of CINAHL, Embase, and Scopus databases was conducted from inception to June 2022, focusing on observational studies that examined multidisciplinary teamworking in surgical services. Data were synthesized narratively. Of the 483 articles screened, eight studies met the inclusion criteria. Most studies focused on oncology teams (n = 4), were conducted in the context of multidisciplinary team (MDT) meetings (n = 4), and employed quantitative methodologies (n = 5). Sample sizes for qualitative studies ranged from 11 to 88 participants, while quantitative studies involved 47 to 1,636 participants; where patient cases were the unit of analysis instead, sample sizes ranged from 50 to 298 cases. The composition of professional groups varied across studies, though all included nurses. Despite the widespread recommendation and adoption of multidisciplinary teamworking in surgical care, only eight relevant studies were identified, and none addressed bariatric surgery specifically. These findings highlight a significant gap and underscore the need for further research on multidisciplinary teamworking in surgical services, particularly in the field of bariatric surgery

Circularity potentials, influential factors, modeling approach and policy interventions of circular supply chain for electric vehicles

Electric vehicles (EVs) provide a primary alternative for mitigating greenhouse gas emissions in the transportation sector. Nonetheless, their extensive use poses concerns, including a rise in throwaway batteries, which, if inadequately managed, may result in heightened human toxicity. Therefore, the establishment of a circular supply chain (CSC) for EVs is crucial for ensuring long-term sustainability. This study seeks to investigate circularity potentials of end of life (EoL) EVs, influential factors, modeling approaches, and policy interventions that promote the implementation of a CSC for EVs based on a systematic review of empirical-based literature following the PRISMA framework. The findings highlight that, under an optimized waste hierarchy, approximately 55.1–59.5 % of EV components can be reused, 24.4−31.8 % repurposed, 55.1−59.5 % remanufactured, and 95.6−96.0 % recycled, leaving about 23.5–24.7 % of components destined for landfills. Five factors pertaining to regulations, economics, environment, technology and infrastructure, ecosystem were identified to be influential for the CSC implementation for EVs. These factors are modeled using either optimization, simulation, or hybrid approach, depending on the modeling objective and settings, in order to comprehend the CSC system, support decision-making and enhance resource recovery strategies. Policy interventions primarily focused on collection and transportation, technology and infrastructure, and economic aspects, have recently been expanded to encompass social interventions, design standardization, and stakeholder collaboration. Given the potential circularity of EV components, the multifaceted factors involving various stakeholders should be addressed in designing and implementing CSC system for a more resource-efficient future of EVs

Bayesian-Symbolic Integration for Uncertainty-Aware Parking Prediction

Accurate parking availability prediction is critical for intelligent transportation systems, but real-world deployments often face data sparsity, noise, and unpredictable changes. Addressing these challenges requires models that are not only accurate but also uncertainty-aware. In this work, we propose a loosely coupled neuro-symbolic framework that integrates Bayesian Neural Networks (BNNs) with symbolic reasoning to enhance robustness in uncertain environments. BNNs quantify predictive uncertainty, while symbolic knowledge—extracted via decision trees and encoded using probabilistic logic programming—is leveraged in two hybrid strategies: (1) using symbolic reasoning as a fallback when BNN confidence is low, and (2) refining output classes based on symbolic constraints before reapplying the BNN. We evaluate both strategies on real-world parking data under full, sparse, and noisy conditions. Results demonstrate that both hybrid methods outperform symbolic reasoning alone, and the context-refinement strategy consistently exceeds the performance of Long Short-Term Memory (LSTM) networks and BNN baselines across all prediction windows. Our findings highlight the potential of modular neuro-symbolic integration in real-world, uncertainty-prone prediction tasks