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This study aims to examine the geographical spread of the EU-funded circular economy projects in the European Union.

The authors use a novel database of research and development projects funded by the European Regional Development Fund related to the circular economy to estimate a fractional response model on data for 231 European regions.

First, the authors detect a geographical pattern in the share of circular economy funds. Second, the authors find that institutional quality, employment, human capital and income may drive the concentration of circular economy research and development funds. Third, the authors find overall differences between technology projects and circular economy projects, suggesting that addressing the circular economy at the subnational level is complex.

This work can be helpful to disseminate Sustainable Development Goals (SDGs). In particular, the authors pay special emphasis on SDGs numbers 11 (Sustainable Cities and Communities) and 13 (Climate Action).

The findings confirm the existence of a geographical spread of the circular economy, which may be useful to move toward regional sustainable development in the European Union.

In the context of the journey toward digital transformation and the realization of a fully connected factory, concepts such as data science, artificial intelligence (AI), machine learning (ML) and even predictive models emerge as indispensable pillars. Given the relevance of these topics, the present study focused on the analysis of customer complaint data, employing ML techniques to anticipate complaint accountability. The primary objective was to enhance data accessibility, harnessing the potential of ML models to optimize the complaint handling process and thereby positively contribute to data-driven decision-making. This approach aimed not only to reduce the number of units to be analyzed and customer response time but also to underscore the pressing need for a paradigm shift in quality management. The application of AI techniques sought to enhance not only the efficiency of the complaint handling process and data accessibility but also to demonstrate how the integration of these innovative approaches could profoundly transform the way quality is conceived and managed within organizations.

To conduct this study, real customer complaint data from an automotive company was utilized. Our main objective was to highlight the importance of artificial intelligence (AI) techniques in the context of quality. To achieve this, we adopted a methodology consisting of 10 distinct phases: business analysis and understanding; project plan definition; sample definition; data exploration; data processing and pre-processing; feature selection; acquisition of predictive models; evaluation of the models; presentation of the results; and implementation. This methodology was adapted from data mining methodologies referenced in the literature, taking into account the specific reality of the company under study. This ensured that the obtained results were applicable and replicable across different fields, thereby strengthening the relevance and generalizability of our research findings.

The achieved results not only demonstrated the ability of ML models to predict complaint accountability with an accuracy of 64%, but also underscored the significance of the adopted approach within the context of Quality 4.0 (Q4.0). This study served as a proof of concept in complaint analysis, enabling process automation and the development of a guide applicable across various areas of the company. The successful integration of AI techniques and Q4.0 principles highlighted the pressing need to apply concepts of digitization and artificial intelligence in quality management. Furthermore, it emphasized the critical importance of data, its organization, analysis and availability in driving digital transformation and enhancing operational efficiency across all company domains. In summary, this work not only showcased the advancements achieved through ML application but also emphasized the pivotal role of data and digitization in the ongoing evolution of Quality 4.0.

This study presents a significant contribution by exploring complaint data within the organization, an area lacking investigation in real-world contexts, particularly focusing on practical applications. The development of standardized processes for data handling and the application of predictions for classification models not only demonstrated the viability of this approach but also provided a valuable proof of concept for the company. Most importantly, this work was designed to be replicable in other areas of the factory, serving as a fundamental basis for the company’s data scientists. Until then, limited data access and lack of automation in its treatment and analysis represented significant challenges. In the context of Quality 4.0, this study highlights not only the immediate advantages for decision-making and predicting complaint outcomes but also the long-term benefits, including clearer and standardized processes, data-driven decision-making and improved analysis time. Thus, this study not only underscores the importance of data and the application of AI techniques in the era of quality but also fills a knowledge gap by providing an innovative and replicable approach to complaint analysis within the organization. In terms of originality, this article stands out for addressing an underexplored area and providing a tangible and applicable solution for the company, highlighting the intrinsic value of aligning quality with AI and digitization.

Christmas is the most consumed event of the year, always full of traditions, namely family ones, which are very significant. In this way, it is intended to find out the importance of traditions at Christmas time and analyze their implications for family businesses.

The study is quantitative in nature, based on a questionnaire survey carried out with 551 Portuguese individuals, over 18 years of age, where different issues related to Christmas traditions and family are addressed.

The results demonstrate that the Christmas traditions scale is made up of four factors: family traditions on Christmas Eve, aspects related to the Christmas spirit, changes in Christmas traditions with the COVID-19 pandemic and traditions of participating in events with family at Christmas. Cod and octopus dishes are the most popular dishes on Christmas Eve. In relation to sweets/desserts, king cake, rabanadas, vermicelli, children's bread and sponge cake are the most common on Christmas Eve.

The study helps to understand Portuguese Christmas traditions, providing knowledge that allows defining strategies for family businesses, improving the experience and relationship with consumers at a special time of year. It is hoped that the trends in Christmas traditions in this study will contribute to unveiling the Christmas spirit, also serve as a marketing image and create curiosity and motivation on the part of other cultures to visit Portugal during this festive season, in order to experience Christmas traditions.