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Social networking sites (SNS) are enjoying growing popularity and have triggered new ethical issues including risks of deception, social grooming, cyber-bullying and surveillance. This development along with the growing power of SNS providers calls for an investigation of the CSR engagement of SNS companies. The chapter examines to what extent three prominent providers – Google, Facebook and Twitter – fulfill their responsibilities.

The chapter draws heavily on a politicized concept of CSR, namely ‘corporate citizenship’ (Crane, Matten, & Moon, 2008a; Matten & Crane, 2005) and ‘political CSR’ (Scherer & Palazzo, 2007, 2011) and discusses the role SNS providers play in administering citizenship rights. The chapter takes a qualitative case study approach.

Facebook, Twitter and Google have not only made clear commitments to act responsibly, they actually enhance the citizenship status of their users in many ways, e.g. by offering a platform for democracy activists. Deficiencies and contradictions also become visible, e.g. SNS providers inhibit citizenship by failing to provide sufficient privacy protection.

The chapter is limited by its case study approach, but provides valuable insights to an industry with considerable influence. It contributes to CSR research by applying and testing the politicized concept of CSR in the context of SNS providers.

Although SNS have received appraisal as effective tools of CSR communication, there has been little attention to CSR policy and practice of the companies providing social networks. This is unfortunate since the activities of SNS providers directly impact on millions of users worldwide.

Disparities in transplant rates across social categories provide limited information about gatekeeping processes in access to kidney transplantation. We hypothesized that early opportunities for discussion of kidney transplantation potentially generate social capital that serves as a resource for patients as they navigate the transplantation pathway.

A national sample of first-year dialysis patients was surveyed and asked if kidney transplantation had been discussed with them before and after starting dialysis treatment. Associations between reported discussion and patient-specific clinical and nonclinical (sociodemographic) indicators of attributed utility for transplantation were investigated, and the association of reported transplant discussion with subsequent transplant waitlisting was analyzed.

Time to placement on the kidney transplant waiting list was significantly shorter for patients who reported that transplantation had been discussed with them before, as well as after, starting dialysis. Likelihood of reported discussion varied by patient age, employment and insurance status, cardiovascular comorbidity burden, and perceived health status; in addition, women were less likely to report early discussion.

It would be valuable to know more about the nature of the transplant discussions recalled by patients to better understand how social capital may be fostered through these discussions.

Indicators of attributed utility for successful transplantation were associated with transplant discussion both before and after starting dialysis, potentially contributing to observed disparities in access to kidney transplantation.

Predialysis nephrology care and patient participation in discussion of kidney transplantation may foster social capital that facilitates navigating the transplantation pathway.

This chapter presents an overview of the Bitcoin and its impacts on the environment and economics from the viewpoint of carrying out a systematic analysis of the literature related to the environmental and economic effect of digital currency. It is aimed to summarize and critically examine the points of view regarding Bitcoin mining, considering its effects on global warming and the social environment, employing peer-reviewed data associated through literatures. As a result, this study provides the chance to analyze the set of knowledge regarding the effects of the Bitcoin mining procedure on the ecosystem in regard to energy use and CO₂ emissions regarding unit root tests and causality test based on nonlinear models. The results show that there exists a nonlinear causal relationship between statistics on Bitcoin mining and the CO₂ emissions. The results also imply that Bitcoin remains to be a tool utilized in the economic environment for a range of objectives despite high energy consumption and some negative environmental impact within the scope of renewable energy; hence, authorities would take Bitcoin mining impacts into account to reduce CO₂ emissions.

Many European businesses are small and medium enterprises (SMEs), contributing significantly to the well-being of local economies and regions. Even so, SMEs face many challenges in fostering innovation and improving performance. Furthermore, the raw material consumption is increasing globally, necessitating the development of strategies that will reduce the number of raw materials extracted and imported while improving the sustainability of small and medium-sized businesses. Consequently, promoting circular economy (CE) strategies, such as industrial symbiosis (IS) partnerships, whereby waste products from other industries serve as a source of raw materials for companies, is critical. Identifying and analysing enablers or drivers that support IS collaborations among SMEs is necessary to achieve this goal. In this regard, the purpose of this study will explore the enablers of IS among SMEs considering sustainability dimensions (environmental, social and economic). As facing a decision-making (DM) problem, the multiple attribute decision-making (MADM) approach was applied in a hesitant fuzzy (HF) environment in this research to answer the research questions. In this regard, in phase 1, IS enablers were identified and extracted using a literature review and experts’ opinions. In phase 2, the hesitant fuzzy Delphi (HFD) method was implemented to screen and finalise the enablers identified. In phase 3, casual relations among final enablers were determined using the hesitant fuzzy ANP (HF-ANP) method. Finally, in phase 4, the relative importance of enablers was calculated using the hesitant fuzzy best–worst method (HF-BWM). Consequently, this study provided potential strategies for IS that can be implemented quickly and used by local authorities to support SMEs in achieving circular waste management.

Purpose: The main aim of the study is to provide a tool for non-financial information in decision-making. We analysed the non-financial data in the annual reports in order to show the usage of this information in financial decision processes.

Need for the Study: Main financial reports such as balance sheets and income statements can be analysed by statistical methods. However, an expanded financial reporting framework needs new analysing methods due to unstructured and big data. The study offers a solution to the analysis problem that comes with non-financial reporting, which is an essential communication tool in corporate reporting.

Methodology: Text mining analysis of annual reports is conducted using software named R. To simplify the problem, we try to predict the companies’ corporate governance qualifications using text mining. K Nearest Neighbor, Naive Bayes and Decision Tree machine learning algorithms were used.

Findings: Our analysis illustrates that K Nearest Neighbor has classified the highest number of correct classifications by 85%, compared to 50% for the random walk. The empirical evidence suggests that text mining can be used by all stakeholders as a financial analysis method.

Practical Implications: Combining financial statement analyses with financial reporting analyses will decrease the information asymmetry between the company and stakeholders. So stakeholders can make more accurate decisions. Analysis of non-financial data with text mining will provide a decisive competitive advantage, especially for investors to make the right decisions. This method will lead to allocating scarce resources more effectively. Another contribution of the study is that stakeholders can predict the corporate governance qualification of the company from the annual reports even if it does not include in the Corporate Governance Index (CGI).

Starting from Gino Arias’s dictum on the uselessness of international trade theory for fascism, this contribution aims to demonstrate two main points. First, the free trade attitude displayed by fascism immediately before and after the “March on Rome” clashed with its nationalist origins. The nationalist movement had supported a strong protectionist policy starting from a rejection of the main principles of marginalist theory. This explains why some issues raised by Pareto and Barone which could have been used as arguments in favor of protectionism were neglected. In turn, this impasse played a major role in the rejection of Mihail Manoilescu’s theory in the thirties. The second point concerns the possibility of some – at least relatively – free theoretical debate on international trade theory and policy. When the regime set itself a clear objective, like the reduction of trade to begin with, and then autarky, the scope for free discussion narrowed to the point of eventually closing. In this context, refusing to support the regime’s choices in economic policy meant resigning oneself to becoming an outcast. A situation offering one more tessera in the complex mosaic of relations between science and politics in authoritarian regimes.

Digitalization can drive the sustainable development of society and industry. However, a lot of the opportunities are tightly related to the risks. This chapter investigates how academic performance in terms of scientific publications at the intersection of digitization, sustainable development, and industry brings added value to the research field. The analysis focuses on the academic papers that research the relationship between digitalization, sustainable development, and industry, analyzing comparatively two primary academic databases, Scopus and Web of Science (WoS). To do that, two samples of scientific publications were investigated. The first sample comprised 703 documents indexed in WoS, while the second was formed by 566 documents indexed in Scopus. The analyzed period for WoS is 1996–2022, while for Scopus is 2000–2022. The empirical results indicated that the research field's primary issues include digitalization, digital transformation, the construction industry, sustainability, COVID-19, and innovation for both WoS and Scopus. The most relevant authors on this cross-cutting subject in the WoS database are Beier G., Aigbavboa C., and Kumar M. In contrast, in the Scopus database, we found Beier G., Ghobakhloo M., and Iranmanesh M. At the same time, the most typical journals are the International Journal of Production Research, Sustainability, and Computers in Industry for WoS and Sustainability, Journal of Cleaner Production, and Procedia CIRP for Scopus.

The sustainable transition towards the circular economy requires the effective use of artificial intelligence (AI) and information technology (IT) techniques. As the sustainability targets for 2030–2050 increasingly become a tougher challenge, society, company managers and policymakers require more support from AI and IT in general. How can the AI-based and IT-based smart decision-support tools help implementation of circular economy principles from micro to macro scales?

This chapter provides a conceptual framework about the current status and future development of smart decision-support tools for facilitating the circular transition of smart industry, focussing on the implementation of the industrial symbiosis (IS) practice. IS, which is aimed at replacing production inputs of one company with wastes generated by a different company, is considered as a promising strategy towards closing the material, energy and waste loops. Based on the principles of a circular economy, the utility of such practices to close resource loops is analyzed from a functional and operational perspective. For each life cycle phase of IS businesses – e.g., opportunity identification for symbiotic business, assessment of the symbiotic business and sustainable operations of the business – the role played by decision-support tools is described and embedding smartness in these tools is discussed.

Based on the review of available tools and theoretical contributions in the field of IS, the characteristics, functionalities and utilities of smart decision-support tools are discussed within a circular economy transition framework. Tools based on recommender algorithms, machine learning techniques, multi-agent systems and life cycle analysis are critically assessed. Potential improvements are suggested for the resilience and sustainability of a smart circular transition.

Social and economic disparities between racial/ethnic groups are a feature of the American context into which immigrants are incorporated and a key determinant of population health. We ask whether racial/ethnic disparities in diabetes vary by nativity and whether native-immigrant disparities in diabetes vary by race and over time in the United States. Using the 2000–2015 National Health Interview Survey, we estimate logistic regressions to examine the interaction of race/ethnicity, nativity, and duration in the US in shaping diabetes patterns. Relative to their native-born co-ethnics, foreign-born Asian adults experience a significant diabetes disadvantage, while foreign-born Hispanic, Black, and White adults experience a significant advantage. Adjusting for obesity, education, and other covariates eliminates the foreign-born advantage for Black and White adults, but it persists for Hispanic adults. The same adjustment accentuates the disadvantage for foreign-born Asian adults. For Black and Hispanic adults, the protective foreign-born effect erodes as duration in the US increases. For foreign-born Asian adults, the immigrant disadvantage appears to grow with duration in the US. Relative to native-born White adults, all non-white groups regardless of nativity see a diabetes disadvantage because the racial/ethnic disadvantage either countervails a foreign-born advantage or amplifies a foreign-born disadvantage. Racial/ethnic differentials in diabetes are considerable and are influenced by each group’s nativity composition. Obesity and (for the foreign-born) time in the US influence these disparities, but do not explain them. These findings underscore the importance of unmeasured, systemic determinants of health in America’s race-conscious society.