Abstract
This article seeks to describe the social representations of Brazilians about the term Governance System (GS). The data were collected through online questionnaires answered by 665 social actors from Brazil. The data analysis used the Social Representation Theory (SRT), operationalized by the techniques of free evocation of words and the Four Houses Framework of Verges, followed by lexical and content analysis. It was identified that in the center of the table there are words highly shared by the social actors about the Governance System: Accountability, Administration, Compliance, Control, Management, Organization, Planning, Processes, Transparency, and Ethics. It is concluded that Accountability is conceived as a structuring element for the effectiveness of the GS. The data suggest the existence of a mute zone in the social representation, since there was a scarcity of words that brought negative expressions about the GS and that deserve future investigations.

Abstract
In the last few decades, Portugal has witnessed an extraordinary quantitative and qualitative transformation in housing provision. The pace of housing construction was so extensive that the contemporary real estate market is currently characterized by an excessive supply, vis-a-vis the resident population. In this study, we discuss the impact of the financial process on the housing sector in comparison with tenancy. We consider transaction prices of the housing assignments, either through acquisition or through tenancy. The recent shock resulting from the pandemic situation did not slow down house prices but caused a slight drop in rents. The model used proposes to analyze the fluctuations in prices and rents in the face of external shocks. In the residential market, the estimation is complex due to the many heterogeneous attributes of residential assets. Non-fluctuating variables, such as size, location, and external demand for homes, explain a large part of the variation in price levels included in the model.

Abstract
Data Science is a tool for organizations to accelerate the development of innovative solutions through collaboration among Small and Medium Enterprises (SMEs), leading to the Industry 4.0 approach. Data is essential in decision-making. Many organizations can't access timely relevant information because they don't own it or there is a lack of collaboration among third parties. Often, data-sharing and collaborative approaches can benefit both, increase the market they operate in, and accelerate innovation. This paper identifies and analyzes the current utility of Data Science for Industry 4.0 from an Open Design perspective to accelerate innovation across different industries. © 2022 The Author(s).

Abstract
For manufacturing systems, Industry 4.0 (I4.0) is currently considered a big challenge and is closely related to intelligent manufacturing or production, alongside a more or less wide set of technologies, methodologies, frameworks, tools and techniques. I4.0 encompasses a diversity of approaches to enable the progress of production systems, resulting in shortened production times, production efficiencies, product quality, customization and flexibility of performance. Due to the general awareness about the importance of enabling intelligent manufacturing alongside sustainable production, sustainability is gaining renewed importance. Due to the importance of the theme, there is a need for an updated review of the main enabling approaches of I4.0 for sustainable manufacturing systems. For this purpose, a literature review was conducted considering the research question: Is there increased attention being given to sustainability issues nowadays in Industry 4.0? Through this work, it was possible to verify the main I4.0 and sustainability pillars considered in academia, which for I4.0 are the integration of horizontal and vertical systems, with 94% of the relevant articles mentioning this pillar. The additive manufacturing and 3D printing was refered in 56% of the relevant articles, and for sustainability, the economic pillar was mentioned with 95%, being the main one, with a large difference from other factors.

Abstract
This paper aims to contribute to the conceptual debate on the connection between social innovation and social entrepreneurship, considering the role of the ‘social’ within this connection. Supported by a systematic literature review (SLR) with an in-depth analysis of 34 articles from Scopus-indexed and Web of Science databases journals, this paper identifies, analyzes and describes the difficulties and opportunities in the social innovation and social entrepreneurship literature. Little is known about the link between both concepts and the influence of the ‘social’ in their development and implementation. This SLR was conducted to identify and describe definitions and patterns. Results show that the connection between social innovation and social entrepreneurship is in its take-off phase, but it still is a fragmented field with a huge lack of consensus. Thus, it will be important to see where the field will head, as this paper aims to be a first step in the understanding of social innovation and social entrepreneurship through a collective and integrated perspective, providing an elucidation of the different perspectives of the literature. © 2022 China Ordnance Society

Abstract
The purpose of this article is to identify the most impactful research on customer churn and to map the conceptual and intellectual structure of its field of study. Data were collected from the WoS database, comprising 338 articles published between 1995 and 2020. Several bibliometric techniques were applied, including analysis of co-words, co-citation, bibliographic coupling, and co-authorship networks. R software and the Bibliometrix/Biblioshiny package were used to perform the analyses. The results identify the most active and influential authors, articles, and journals on the topic. More specifically, through co-citations and bibliographic coupling, it was possible to map the oldest articles (retrospective analysis) and the current research front (prospective analysis). The retrospective analysis, based on co-citations, revealed that the foundations of this research field are constructs such as quality of service, satisfaction, loyalty, and changing behaviors. The prospective analysis, performed through bibliographic coupling, revealed that current research is embedded in predictive analysis, clusters, data mining, and algorithms. The results provide robust guidance for further investigation in this field.