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The purpose of this paper is to assess the applicability of care quality concepts as contract award criteria for public procurement of health and social care, using the case of Sweden.

Based on a literature review, European and Swedish legal texts, government regulations as well as 26 Swedish court review cases concerning care procurement have been analysed.

Methods used for assessing care quality are seldom useful for predicting the quality to be delivered by a potential contractor. Legal principles of transparency and equal treatment of tenderers make it necessary to apply strict requirements for verification.

Results refer primarily to a Swedish context but could be applicable throughout the EU. Further studies of relations between award criteria and public/private collaborative practices for improving care quality during contractual periods are desirable.

Local and regional procurement officials should benefit from a better understanding of how quality criteria should be designed and applied to the award procedures for care contracts. Care providers in the private sector would also be able to develop their quality strategies and present their abilities more efficiently when tendering for public contracts.

Issues of quality of health and social care are of obvious importance for social sustainability. Public awareness of care quality problems is evident and often a cause of media concern.

This investigation pinpoints the difference between traditional care quality thinking and the legal principles underlying contract award in public procurement of care services.

The primary purpose of this paper is to identify the so-called core authors and their publications according to pre-defined criteria and thereby direct the users to the fastest and easiest way to get a picture of the otherwise pervasive field of bankruptcy prediction models. The authors aim to present state-of-the-art bankruptcy prediction models assembled by the field's core authors and critically examine the approaches and methods adopted.

The authors conducted a literature search in November 2022 through scientific databases Scopus, ScienceDirect and the Web of Science, focussing on a publication period from 2010 to 2022. The database search query was formulated as “Bankruptcy Prediction” and “Model or Tool”. However, the authors intentionally did not specify any model or tool to make the search non-discriminatory. The authors reviewed over 7,300 articles.

This paper has addressed the research questions: (1) What are the most important publications of the core authors in terms of the target country, size of the sample, sector of the economy and specialization in SME? (2) What are the most used methods for deriving or adjusting models appearing in the articles of the core authors? (3) To what extent do the core authors include accounting-based variables, non-financial or macroeconomic indicators, in their prediction models? Despite the advantages of new-age methods, based on the information in the articles analyzed, it can be deduced that conventional methods will continue to be beneficial, mainly due to the higher degree of ease of use and the transferability of the derived model.

The authors identify several gaps in the literature which this research does not address but could be the focus of future research.

The authors provide practitioners and academics with an extract from a wide range of studies, available in scientific databases, on bankruptcy prediction models or tools, resulting in a large number of records being reviewed. This research will interest shareholders, corporations, and financial institutions interested in models of financial distress prediction or bankruptcy prediction to help identify troubled firms in the early stages of distress.

Bankruptcy is a major concern for society in general, especially in today's economic environment. Therefore, being able to predict possible business failure at an early stage will give an organization time to address the issue and maybe avoid bankruptcy.

To the authors' knowledge, this is the first paper to identify the core authors in the bankruptcy prediction model and methods field. The primary value of the study is the current overview and analysis of the theoretical and practical development of knowledge in this field in the form of the construction of new models using classical or new-age methods. Also, the paper adds value by critically examining existing models and their modifications, including a discussion of the benefits of non-accounting variables usage.

The purpose of this study is to develop a method to assess social performance. Traditionally, environment, social and governance (ESG) rating providers use subjectively weighted arithmetic averages to combine a set of social performance (SP) indicators into one single rating. To overcome this problem, this study investigates the preconditions for a new methodology for rating the SP component of the ESG by applying machine learning (ML) and artificial intelligence (AI) anchored to social controversies.

This study proposes the use of a data-driven rating methodology that derives the relative importance of SP features from their contribution to the prediction of social controversies. The authors use the proposed methodology to solve the weighting problem with overall ESG ratings and further investigate whether prediction is possible.

The authors find that ML models are able to predict controversies with high predictive performance and validity. The findings indicate that the weighting problem with the ESG ratings can be addressed with a data-driven approach. The decisive prerequisite, however, for the proposed rating methodology is that social controversies are predicted by a broad set of SP indicators. The results also suggest that predictively valid ratings can be developed with this ML-based AI method.

This study offers practical solutions to ESG rating problems that have implications for investors, ESG raters and socially responsible investments.

The proposed ML-based AI method can help to achieve better ESG ratings, which will in turn help to improve SP, which has implications for organizations and societies through sustainable development.

To the best of the authors’ knowledge, this research is one of the first studies that offers a unique method to address the ESG rating problem and improve sustainability by focusing on SP indicators.

This study examines Texas FFA officers’ perceptions regarding the traits and characteristics that good followers possess. A content analysis of officer responses to an open-ended question found that these young leaders have a limited level of understanding of what constitutes a good follower. Furthermore, female respondents placed a greater emphasis on supporting the leader and being respectful, whereas male respondents emphasized being a team player. Males also emphasized being involved in the chapter as a desirable trait more often than females. This suggests that females are more focused on ideas that are leader-supportive and males are more action driven. Males focused more on being a team and achievement. The study demonstrates a need to emphasize followership in leader development and education curricula. Youth organizations with similar leadership development programs can better equip their future leaders with the knowledge needed to be a follower-focused leader by teaching them how to identify good followership. As youth organizations become more follower-focused, failure to introduce followership instruction into the leadership classroom is a disservice to students.