Abstract
Purpose: The assessment and observation of critical service factors within the retail industry have garnered increased importance in recent times, due to their perceived ability to shape superior future strategies. The aim of this study is to investigate the service elements that are deemed essential by consumers in the retail sector, specifically targeting the grocery retail industry. Design/Methodology/Approach: Our methodological framework incorporates a systematic review of previous literature and a meta-analysis of past studies that highlight the pivotal service elements within the chosen industry. Following the evaluation of existing literature, 55 studies met the inclusion criteria and were selected for further investigation. The systematic review first compiled information from multiple studies, which was then followed by a meta-analysis. This enabled us to statistically analyze the empirical data from the chosen studies, thereby drawing significant conclusions. Findings: The analyses pinpoint that elements such as personal interaction attributes, product quality and availability, and reliable service are of utmost importance to consumers. Interestingly, customer satisfaction was the only outcome that was positively influenced by all the examined service attributes. Additionally, our findings underscore that certain moderators, such as geographic region and timing of the study, sway the relationship between service attributes and customer outcomes. Originality: Despite numerous meta-analyses attempting to pinpoint the key service attributes for consumers, to the best of our understanding, this study is the first to focus on the retail industry, specifically on hypermarkets, supermarkets, or grocery stores. Therefore, this research bridges a gap in the literature and offers a significant contribution to the academic community by proposing an agenda for future research on customer service factors. It also provides invaluable insight for retail managers, outlining numerous practical implications and offering guidance.

Abstract
Globalisation, urbanisation and the recent COVID-19 pandemic has been raising the demand for logistic activities. This change is affecting the entire supply chain, especially the last-mile step. This step is considered the most expensive and ineffective part of the supply chain and a source of negative economic, environmental and social externalities. This article aims to characterise the sustainable urban last-mile logistics research field through a systematic literature review (N = 102). This wide and holistic review was organised into six thematic clusters that identified the main concepts addressed in the different areas of the last-mile research and the existence of 14 solutions, grouped into three types (vehicular, operational, and organisational solutions). The major findings are that there are no ideal last-mile solutions as their limitations should be further explored by considering the so-called triple bottom line of sustainability; the integration and combination of multiple last-mile alternative concepts; or by establishing collaboration schemes that minimise the stakeholders' conflicting interests.

Abstract
Globalization and the COVID-19 pandemic led to an increased number of consumers using e-commerce services. This trend has been raising the demand for logistic activities, especially on the last-mile. This part of the supply chain is expensive and ineffective, and a source of negative externalities such as air and noise pollution, traffic congestion and accidents. The anticipation of innovative solutions can help to mitigate these costs. In this context, this paper provides a systematic literature review of the existing literature regarding emerging solutions for last-mile parcel delivery. For guiding the development of more sustainable last-mile parcel distribution, and to provide some insights for future research, we identified and summarized the emerging concepts within this field domain. The results show that innovative solutions have been emerging at different levels: (i) definition of new crowdsourcing-based models, (ii) use of new types of vehicles, and (iii) development of optimization systems based on data collection and the combination of different technologies. Moreover, recent studies show that new strategies are being developed focusing on using consumers as active actors of delivery; non-road and autonomous vehicles are promising concepts in last-mile operations; and different logistic operations, such as vehicle routing, are being optimized with data analytics, cloud technology and mobile apps.

Abstract
By 2050, and in the context of decarbonization and carbon neutrality, many companies worldwide are looking for low-carbon alternatives. Transport companies are probably the most challenging due to the continuing growth in global demand and the high dependency on fossil fuels. Some alternatives are emerging to replace conventional diesel vehicles and thus reduce greenhouse gas emissions and air pollutants. One of these alternatives is the adoption of compressed natural gas (CNG). In this paper, we provide a detailed study of the current emissions from the largest bus fleet company in the metropolitan area of Oporto. For this analysis, we used a top-down and a bottom-up methodology based on EMEP/EEA guidebook to compute the CO2 and air pollution (CO, NMVOC, PM2.5, and NOx) emissions from the fleet. Fuel consumption, energy consumption, vehicle slaughter, electric bus incorporation, and the investments made were taken into consideration in the analyses. From the case study, the overall reduction in CO2 emission was just 6.3%, and the emission factors (air pollutants) from CNG-powered buses and diesel-powered buses are closer and closer. For confirming these results and question the effectiveness of the fleet transitions from diesel to CNG vehicles, we analysed two scenarios. The obtained results reveal the potential and effectiveness of electric buses and other fuel alternatives to reduce CO2 and air pollution.

Abstract
E-commerce growth is raising the demand for logistic activities, especially in the last-mile, which is considered the most ineffective part of the supply chain and a negative externalities source. Although various solutions aim to address these issues, selecting the best one is challenging due to multiple perspectives, conflicting criteria, trade-offs, and complex and sensitive urban contexts. This article proposes a 4-level hierarchical model based on the triple bottom line of sustainability that may assist decision-makers in selecting the most adequate last -mile solution for historic centers. The model was defined based on a systematic literature review; evaluated by interviewing a set of experts; and quantified according to an AHP-TOPSIS approach. This quantification focused on the historic center of Porto, Portugal. The experts considered all three sustainability dimensions similarly important. Air pollution was the most valued sub-criterion whereas Visual pollution was the least. 67 decision-maker profiles were defined, showing that environmentally oriented decision-makers prefer cargo bikes, while decision-makers who prioritize economic and social factors prefer parcel lockers. All last-mile solutions considered in the model yielded similar results, therefore suggesting a combined distribution strategy. Nevertheless, the use of parcel lockers is the most favorable solution for Porto's historic center.

Abstract
Machine learning algorithms offer the capability to analyze large volumes of real-time data, providing transport authorities with valuable insights into traffic conditions, congestion hotspots, and incident detection from diverse data sources. However, these algorithms face challenges related to data quality and reliability. We conducted a comparative analysis of machine-learning models that can be used to identify and filter transportation content from social media or other sources that can provide small and concise text. The filtrated result can then feed models and/or tools used to improve and automate traffic control, operational management, and tactical management decision-making. We consider factors such as run time, generalization capacity, and performance metrics as criteria to assess their suitability for different decision levels. The analysis is supported by a dataset consisting of Twitter content. The predictions from three groups of algorithms are evaluated: traditional machine learning algorithms (Support Vector Machines, Logistic Regression, and Random Forest), a fine-tuned Google BERT model, and Google BERT models without training (BERT-base and BERT-large). The tests are performed using New York, London, and Melbourne data. The findings of this research aim to assist decision-makers in making informed choices when selecting the most appropriate method to filtrate information subsequently used for models that contribute to different traffic management tasks.