Abstract
As cities grow and sustainability becomes a key driver of urban policy, active modes of transport such as walking and cycling are increasingly promoted. However, current route planning applications rarely consider factors beyond time and distance. This paper presents the design and evaluation of a mobile application prototype that supports multicriteria route planning for active transport modes. The proposed solution incorporates user-defined weights for dimensions such as safety, comfort, accessibility, and environmental quality. To ensure adaptability and up-to-date information, the study also explores the feasibility of crowdsourcing as a complementary data source. A mixed-method approach was followed, including literature review, user surveys (n=242), interface prototyping, and usability testing with real users. The results demonstrate strong user interest in contributing to data updates, especially when motivated by non-monetary incentives such as gamified rankings. The final prototype was positively evaluated for usability and interface quality. This research confirms the potential of user-centered, crowdsourcing-enhanced route planning to improve the experience of active mobility users and support sustainable urban mobility goals. Copyright © 2025. Published by Elsevier B.V.

Abstract
Urban mobility in large cities faces increasing pressure due to population growth and congestion. Automated Fare Collection (AFC) systems offer a rich source of data for understanding public transport usage and informing data-driven improvements. This paper presents a case study in Fortaleza, Brazil, where we explore AFC smart card data to predict users’ next bus trips and travel volume. We develop a machine learning pipeline combining feature engineering and classification/regression models. A comparative evaluation of algorithms, including Random Forest, XGBoost, and Support Vector Machines, shows that decision-tree-based models achieve the best performance, particularly in handling noisy and imbalanced data. Our approach considers both user-level predictions and cluster-based analyses to improve model generalizability across user types. The results demonstrate the potential of AFC data to enhance transit planning, reduce overcrowding, and personalize mobility services. This study contributes to the growing body of research on smart mobility analytics in developing urban contexts. Copyright © 2025. Published by Elsevier B.V.

Abstract
The rapid expansion of information production presents a growing challenge in identifying high-quality, relevant studies necessary for a solid research foundation in Systematic Literature Reviews (SLR). Traditional methods often struggle with academic publications' increasing volume and dynamic nature, necessitating more efficient analytical tools. Artificial Intelligence (AI) and Natural Language Processing (NLP) offer significant potential in streamlining literature reviews through advanced analytical techniques. This work explores how AI and NLP tools enhance article screening and information synthesis, particularly through Retrieval-Augmented Generation (RAG). While large language models demonstrate strong text generation capabilities, they frequently lack comprehensive contextual understanding. RAG addresses this limitation by retrieving precise information, enabling more accurate and context-aware literature reviews. A novel approach is proposed that transforms the traditionally linear SLR workflow into a dynamic, continuously updatable bundle- a unified framework that integrates search, screening, data extraction, and synthesis. This approach is inspired by the RAPTOR Python package, which recursively embeds, clusters, and summarizes text to construct a hierarchical knowledge structure. The adapted model, DSA.VE, extends RAPTOR's capabilities to improve contextual summarization and structured synthesis, enhancing its applicability in multidisciplinary research fields. To demonstrate the effectiveness of this approach, a case study examines the potential of methanol as an alternative fuel in transport systems. The results highlight how AI-driven methodologies facilitate large-scale literature synthesis and knowledge integration. By leveraging AI, this work contributes to developing more efficient, systematic, and scalable literature review processes, addressing a critical challenge in modern research.

Abstract
ABSTRACT This study explores the impact of Green Knowledge Sharing (GKS) on Organizational Performance (OP), considering the mediating roles of Green Organizational Culture (GOC) and Technological Innovation (TI). Addressing current gaps in the literature, the research extends beyond sector-specific analyses and incorporates a cross-country perspective, examining 297 small and medium-sized enterprises (SMEs) in Portugal, Spain, and the United Kingdom. Additionally, this study acknowledges the influence of digital transformation in enhancing GKS, a factor often overlooked in previous research. By adopting a Structural Equation Modeling (SEM) approach, this article confirms a direct and positive effect on both OP and GOC, with GOC further influencing OP, establishing its mediating role in this relationship. However, the relationships between GKS and TI, as well as the indirect effect of GKS on OP through TI, are not supported. These findings offer theoretical advancements by broadening the conventional understanding of OP beyond financial metrics and present practical implications for SME managers, highlighting strategies to foster a green organizational culture and leverage technological innovation for sustainable performance.

Abstract
As recommender systems play an increasingly central role in shaping information exposure on platforms like YouTube, understanding the nature of the content they promote, especially in sensitive contexts, requires scalable and reliable labelling methods. This paper investigates the use of Large Language Models (LLM) to label YouTube videos based solely on their metadata. We propose a committee-based approach that aggregates predictions from an ensemble of seven state-of-the-art LLMs through majority voting. Using a novel dataset collected via simulated user interactions on YouTube, we analyse model agreement, labelling behavior, and the influence of model size. To assess label reliability, we also investigate the semantic coherence of label assignments. Our results show that LLM committees produce highly consistent labels in low-disagreement settings. These findings highlight both the promise and limitations of LLM-based annotation for auditing social networks.

Abstract
We study a major cyberattack at a large omnichannel grocery retailer that triggered a one-week shutdown of its online store and a corporate data breach. Using high-frequency panel data on more than 20,000 loyalty customers over 2019 and 2022, we implement a within-customer difference-indifferences design. Over the 13-week post-attack window, online transactions and online revenue both declined by approximately 10% relative to the counterfactual, with effects persisting across short-, medium-, and long-term horizons and no evidence of recovery. This contrasts with firm-level event studies, which show only short-term market reactions. The offline channel provided limited mitigation of the effects. Customers primarily substituted for alternative retailers rather than reallocating purchases within the firm, resulting in a net revenue decline for our industry partner. Effects were heterogeneous and customers with a higher concentration of online purchases, higher patronage (greater online purchase frequency and spending), and subscription ties exhibited greater shopping resilience, whereas customers with predictable shopping patterns who likely would have shopped during the affected period (and thus directly experienced the outage) were more prone to churn. This concentration of the effect among customers most likely to have been directly affected by the outage is more consistent with a substitution mechanism triggered by the disruption itself than with a generalised trust-loss interpretation, which would predict a similar response across informed customers regardless of their individual exposure to the disruption window.