Abstract
This study seeks to comprehensively analyze the multidimensional determinants underlying perceptions of safety, security, and comfort in transport mode choice, specifically focusing on private transport, public transport and walking. The research begins with an extensive literature review to identify and delve into the factors influencing perceptions of safety, security, and comfort across various transport modes. This inquiry is further enhanced by organizing two focused group sessions. A total of 35 key factors were identified, forming the basis for subsequent investigation. The study then progressed to the development and administration of a survey aimed at capturing responses from a diverse audience, with the goal of exploring the factors influencing perceptions related to different transport modes. A total of 302 responses were collected and meticulously analyzed to discern the factors impacting various relationships and to identify consistent perceptions across diverse transport modes. Additionally, a factor analysis was conducted to validate the findings derived from the data. The outcomes of this research constitute a significant contribution to the existing literature, offering valuable insights that pave the way for a more holistic understanding of the factors guiding transport mode choices. © The Author(s) 2025.

Abstract
Purpose - Customer-provider relationships unfold through multiple touchpoints across different channels. However, some touchpoints are more important than others. Such important touchpoints are viewed as moments of truth (MOTs). This study examines the impact of a series of touchpoints on an MOT, and the role MOTs play in determining future profitability and other behavioral outcomes (e.g. customer retention and customer cross-buy) in a business-to-business (B2B) context. Design/methodology/approach - Building upon social exchange theory, a conceptual model is proposed and tested that examines the impact of human, digital, and physical touchpoints and past MOTs on customer evaluation of a current MOT and on future customer outcomes. This research employs a longitudinal methodology based on a unique panel dataset of 2,970 B2B customers. Findings - Study results show that all touchpoints significantly contribute to MOTs, while human and physical touchpoints maintain their primacy during MOTs. The impact of MOTs on future customer outcomes is also demonstrated. Practical implications - This study highlights the need for prioritizing human and physical touchpoints in managing MOTs, and for carefully managing MOTs across time. Originality/value - Given its B2B outlook and longitudinal approach, this research contributes to the multichannel and interactive marketing literature by determining relevant touchpoints for B2B customers.

Abstract
Engagement in the ongoing energy transition is particularly challenging for energy-poor citizens. As such, there is a pressing need for a better understanding of their experiences and for strategies that enable their engagement. In this study, we identify different groups of citizens based on their energy poverty issues and examine their engagement behaviors (seeking information, proactive managing, sharing feedback, helping others, and advocating). Using cluster analysis and multiple correspondence analysis, we analyzed a sample of 915 citizens from eight European cities participating in a Horizon2020 EU project (Alkmaar-NL, Bari-IT, Celje-SI, Evora-PT, Granada-ES, Hvidovre-DK, Ioannina-GR, & Uacute;jpest-HU). Several groups of citizens reported either multiple energy issues, a single issue (energy bills, insulation, cooling, heating), or no issues, and the statistical tests showed significant differences across these groups in terms of engagement in seeking information, helping, and advocating. Moreover, we identified that certain groups tend to have specific levels of engagement (high, medium, low) and that sharing feedback generally has a low level of engagement. Overall, this study provides empirical insights into how energy-poor citizens exercise agency through engagement behaviors and offers actionable insights for designing measures to mitigate energy poverty in complementarity with technical and economical solutions.

Abstract
In today's highly competitive fashion retail market, it is crucial to have accurate demand forecasting systems, namely for new products. Many experts have used machine learning techniques to forecast product sales. However, sales that do not happen due to lack of product availability are often ignored, resulting in censored demand and service levels that are lower than expected. Motivated by the relevance of this issue, we developed a two-stage approach to forecast the demand for new products in the fashion retail industry. In the first stage, we compared four methods of transforming historical sales into historical demand for products already commercialized. Three methods used sales-weighted averages to estimate demand on the days with stock-outs, while the fourth method employed an Expectation-Maximization (EM) algorithm to account for potential substitute products affected by stock-outs of preferred products. We then evaluated the performance of these methods and selected the most accurate one for calculating the primary demand for these historical products. In the second stage, we predicted the demand for the products of the following collection using Random Forest, Deep Neural Networks, and Support Vector Regression algorithms. In addition, we applied a model that consisted of weighting the demands previously calculated for the products of past collections that were most similar to the new products. We validated the proposed methodology using a European fashion retailer case study. The results revealed that the method using the Expectation-Maximization algorithm had the highest potential, followed by the Random Forest algorithm. We believe that this approach will lead to more assertive and better-aligned decisions in production management.

Abstract
Municipal solid waste (MSW) management has become a critical issue today, posing substantial economic, environmental, and social challenges. Identifying and analyzing dominant themes in this field is essential for advancing research and policies towards sustainable MSW management practices. This study aims to explore the key issues related to MSW management that have been addressed by both the scientific community and policymakers through funded projects. By doing so, the study seeks to guide the scientific community as a knowledge producer and the EU as a key funder. Two Latent Dirichlet Allocation (LDA) models were applied to analyze the themes from two corpora: one representing scientific literature and another focusing on EU-funded projects. Additionally, this analysis was complemented by a quantitative estimation of the similarity between the two corpora, providing a measure of alignment between the scientific community and policymakers. The results generally indicate that the two spheres are aligned and highlight the diversity of topics explored by the scientific community. Nevertheless, it is concluded that there are opportunities for further research on specific topics, such as leaching and the extraction of heavy metals. Additionally, the popularity of topics identified in European Union-funded projects has fluctuated considerably over time, focusing primarily on waste management rather than its prevention. In light of these findings, waste prevention emerges as a promising avenue for future EU-funded research initiatives.

Abstract
The retention of public transport users is widely acknowledged as a paramount challenge in the path towards the establishment of more sustainable cities and societies. In this setting, in which no contractual relationship with customers exists, an early and accurate prediction of whether a customer will remain with the company or leave, assumes great significance for businesses to develop effective retention strategies. This work focuses on this topic by identifying potential churners based on their past travel behavior. To achieve this, we developed a set of classification models using various machine learning techniques. These models were then employed as base learners within a stacking ensemble. All classifiers were developed with a profit-driven approach, optimizing for expected maximum profit. Finally, we calculated Shapley Additive Explanation values to enhance the interpretability of the proposed classifiers. The performance of the predictive models was evaluated using the data of taxi services recorded in a Portuguese city for 52 months. A broad range of predictors is proposed, including recency and frequency measures of taxi usage as well as others related to customers' satisfaction level. The predictive power of the models was also assessed for specific proportions of higher risk customers. All models have shown the capability to identify churners accurately. This study innovates in evaluating the one-to-one service provider company-customer relationship in the context of taxi industry. Retention actions to promote customers loyalty and enhance retention are also suggested. © 2025 The Author(s)