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The circular economy business models (CEBMs) provide ways for firms operating in the construction industry to move from a linear to a circular approach. Thus, this study aims to explore CEBM research within the construction sector to show the focus area of studies, highlighting new areas that require attention.

This study adopted a bibliometric approach, using the Scopus database as the data source. The keywords used for paper extraction from the database were “circular economy business” OR “circular business” AND “model” OR “models” AND “construction industry” OR “building industry”. The VOSviewer software was then used to prepare a co-occurrence and co-authorship map based on the bibliographic data gathered.

The study’s findings reveal five research clusters in the construction industry. These clusters include circular construction intelligence, modular business modelling, eco-construction, sustainable construction economics and smart energy-efficient buildings. The two most cited scholars had two publications each, while the top journals are the Journal of Cleaner Production and Sustainable Production and Consumption. This study concludes that there is a need for research within the construction sector to focus on CEBMs’ archetypes and frameworks. This will enable a smooth transition from linear to circular business models in the sector.

The information was gathered from a single database, Scopus; hence, using other databases, including Web of Science, Google Scholar and Dimensions, might produce more articles for examination and, consequently, different findings on the subject under investigation.

These findings would assist researchers in considering the areas mentioned, which are yet to receive attention, and, by extension, enhance economic development while maintaining environmental sustainability.

This paper made a significant contribution to the body of knowledge by identifying scholars and platforms that have been instrumental in advancing CEBM research and highlighting new areas that require attention in the construction sector.

This study aims to examine the impact of the beginning of the Russia–Ukraine war and the Wagner Group’s attempted military coup against Putin’s regime on the European defense sector, consisting of weapons manufacturers.

The authors use the event study methodology to quantify the impact. That is, the authors assume that markets are efficient, and abnormal stock returns around the event dates capture the magnitudes of the impacts of the two events studied on European defense sector companies. The authors use the capital asset pricing model and two different multifactor models to estimate expected stock returns, which serve as the benchmark necessary to obtain abnormal returns.

The start of the war on February 24, 2022, when the Russian forces invaded Ukraine, was followed by high positive abnormal returns of up to 12% in the next few days. The results are particularly strong if multiple factors are used to control for the risk of the defense stocks. Conversely, the authors find a negative impact of the rebellion initiated by the mercenary Wagner Group’s chief, Yevgeny Prigozhin, on June 23, 2023, on the abnormal returns of defense industry stocks on the first trading day after the event.

To the best of the authors’ knowledge, this is the first study of the impact of the Russia–Ukraine war on the defense sector. Furthermore, this is the first study to measure the financial implications of the military coup initiated by the Wagner Group. The findings contribute to a rapidly growing literature on the financial implications of military conflicts around the world.

This study investigated the visibility of carbon emissions allowances accounting in the financial reports of 32 clean development mechanism (CDM) projects in the UAE to uncover the obstacles to setting consistent standards for carbon emission accounting. As carbon emissions are monetized as credits, consistent accounting standards can aid decision-makers in the development of carbon emission mitigation strategies.

This study used a grounded theoretical framework for exploring the terms used in the policy documents of international accounting bodies regarding accounting standards and guidelines for carbon emission credits. Raw qualitative data were gathered, and an inductive approach was used by analyzing documents from various sources using the qualitative data text analysis software QDA Miner 6.

The findings showed that the financial statement reports of the corporations did not include disclosure of the carbon credit account. This omission was due to the lack of global standardization of carbon credit accounts and emission allowance recognition. This may hinder the production of a comprehensive report containing accurate and valuable financial information relevant to all stakeholders.

The study is among the first to use a grounded theoretical framework to investigate whether corporations are applying common standards and guidelines for carbon emissions accounting.

The purpose of this study is to examine the state of research into adoption of machine learning systems within the health sector, to identify themes that have been studied and observe the important gaps in the literature that can inform a research agenda going forward.

A systematic literature strategy was utilized to identify and analyze scientific papers between 2012 and 2022. A total of 28 articles were identified and reviewed.

The outcomes reveal that while advances in machine learning have the potential to improve service access and delivery, there have been sporadic growth of literature in this area which is perhaps surprising given the immense potential of machine learning within the health sector. The findings further reveal that themes such as recordkeeping, drugs development and streamlining of treatment have primarily been focused on by the majority of authors in this area.

The search was limited to journal articles published in English, resulting in the exclusion of studies disseminated through alternative channels, such as conferences, and those published in languages other than English. Considering that scholars in developing nations may encounter less difficulty in disseminating their work through alternative channels and that numerous emerging nations employ languages other than English, it is plausible that certain research has been overlooked in the present investigation.

This review provides insights into future research avenues for theory, content and context on adoption of machine learning within the health sector.

The paper provides a detailed historical account of Douglass C. North's early intellectual contributions and analytical developments in pursuing a Grand Theory for why some countries are rich and others poor.

The author approaches the discussion using a theoretical and historical reconstruction based on published and unpublished materials.

The systematic, continuous and profound attempt to answer the Smithian social coordination problem shaped North's journey from being a young serious Marxist to becoming one of the founders of New Institutional Economics. In the process, he was converted in the early 1950s into a rigid neoclassical economist, being one of the leaders in promoting New Economic History. The success of the cliometric revolution exposed the frailties of the movement itself, namely, the limitations of neoclassical economic theory to explain economic growth and social change. Incorporating transaction costs, the institutional framework in which property rights and contracts are measured, defined and enforced assumes a prominent role in explaining economic performance.

In the early 1970s, North adopted a naive theory of institutions and property rights still grounded in neoclassical assumptions. Institutional and organizational analysis is modeled as a social maximizing efficient equilibrium outcome. However, the increasing tension between the neoclassical theoretical apparatus and its failure to account for contrasting political and institutional structures, diverging economic paths and social change propelled the modification of its assumptions and progressive conceptual innovation. In the later 1970s and early 1980s, North abandoned the efficiency view and gradually became more critical of the objective rationality postulate. In this intellectual movement, North's avant-garde research program contributed significantly to the creation of New Institutional Economics.