Abstract
The digital transformation of manufacturing activities is expected to bring large societal benefits in terms of productivity and sustainability. However, uptake of digital technologies is slower than desirable. As a result, governments are taking action to try to overcome some of the barriers to adoption. However, the mechanisms through which government may act are quite diverse. In this paper, we compare the national strategies across the 27 countries members of the European Union. We map each country's initiative to 14 barriers to the adoption of digital technologies in manufacturing observed in the literature. We observe that most institutional efforts focus on providing funding, developing new regulatory frameworks related to data privacy and security, and creating human capital. Some known barriers to adoption observed at the firm level, such as the lack of off-the-shelf solutions, or the need for retrofitting old equipment, are largely overlooked. We do not find any relationship between the number of initiatives proposed by each country, and the country's existing level of digitalization. We conclude by proposing several policy recommendations, as well as directions for future research.

Abstract
Digital transformation creates a number of barriers that need to be surpassed by companies from the technological and organizational points of view. Concurrently, the complexity and nature of current market environments often demand new products, services, processes and business models, oftentimes supported by digital technologies. The objective of this paper is to contribute to a better understanding on the impact of a severe global crisis on the digital technologies’ adoption process (and their associate drivers and barriers), with a special look on the strategies adopted by companies in terms of supply chain resilience. Specificities of the Portuguese industry are discussed through the analysis of five case studies. © 2023, IFIP International Federation for Information Processing.

Abstract
This work presents a deep learning framework for analyzing urban mobility by extracting knowledge from messages collected from Twitter. The framework, which is designed to handle large-scale data and adapt automatically to new contexts, comprises three main modules: data collection and system configuration, data analytics, and aggregation and visualization. The text data is pre-processed using NLP techniques to remove informal words, slang, and misspellings. A pre-trained, unsupervised word embedding model, BERT, is used to classify travel-related tweets using a unigram approach with three dictionaries of travel-related target words: small, medium, and big. Public opinion is evaluated using VADER to classify travel-related tweets according to their sentiments. The mobility of three major cities was assessed: London, Melbourne, and New York. The framework demonstrates consistently high average performance, with a Precision of 0.80 for text classification and 0.77 for sentiment analysis. The framework can aggregate sparse information from social media and provide updated information in near real-time with high spatial resolution, enabling easy identification of traffic-related events. The framework is helpful for transportation decision-makers in operational control, tactical-strategic planning, and policy evaluation. For example, it can be used to improve the management of resources during traffic congestion or emergencies.

Abstract
This study examined the relationship between Environmental Performance (EP) and Financial Performance (FP) in the European food industry. The food industry is essential for population sustenance, but the rising population and the consequent increase in food production demand have implications for climate change. The aim of this study was to determine if businesses that consume water more efficiently and have lower CO2 emission intensities might experience improved financial performance. Financial and environmental data were sourced from external databases and company reports, and both quantile regression and correlation analyses were conducted. The results reveal that various sectors within the food industry exhibit different linkages between Environmental Performance and Financial Performance. Furthermore, our findings indicate that water use efficiency can significantly influence financial performance, either positively or negatively, while CO2 emission intensity did not exhibit a definitive impact on Financial Performance.

Abstract
This research investigates the implementation of lean practices in services in order to identify those that have a greater influence on company performance. Regression analysis with data from a systematic literature review was the basis to study the relationship between lean and performance. For this purpose, a total of 104 case studies were considered. A main finding was that some lean practices, such as “voice of the customer” and “cross-functional teams” have a significant positive influence on performance. Also, the results suggest that the more engaged managers are and the more they invest in training, the better company performance will be. Finally, one may also conclude that knowledge about the determinants of lean management will allow managers to be aware of what is decisive to improve company performance. © 2023, World Scientific and Engineering Academy and Society. All rights reserved.

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.