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This paper aims to examine how bank stress tests affect bank tax planning.

The study uses US bank stress test bank size thresholds and a regression discontinuity design to investigate the effect of the Dodd-Frank Act and the instituted bank stress tests on bank tax planning. We use different measures of tax planning, including bank-specific measures and measures of tax avoidance, tax aggressiveness, and effective tax planning from recent literature. Our regression discontinuity and difference-in-differences regression analyses include bank and year fixed-effects and lagged bank characteristics to control for potential endogeneity.

This study finds that stress tests have the unintended consequences of intensifying tax planning and increasing tax avoidance. Stress-test banks increase tax avoidance by accelerating charge-offs, net interest, and non-interest expenses. However, this increase in tax planning is not optimally maximized, leading to lower effective tax planning compared to non-stress-test banks. Banks with a substantial increase in tax avoidance under the Dodd–Frank Act tend to increase their risk, investing in high-risk-weight assets and lending in riskier loan categories. These findings are consistent with tax minimization conditions under added regulatory attention and policy uncertainty.

Literature on bank tax planning is limited. Most tax avoidance literature excludes financial institutions such as bank holding companies mainly due to differences in business practices and regulatory frameworks. This study is the first to investigate tax planning behavior among US banks. The current study thus extends the research field by examining the effect of bank transparency regulations, such as bank stress tests, on bank tax planning activities. Our findings have a direct bank policy implication. They show that stress testing has the unintended consequences of increasing tax planning activities and consequently increasing risk-taking on banks with high tax avoidance, which goes against the goals of stress testing regulations.

This chapter addresses the role of corporate social responsibility (CSR) in tourism and hospitality, using examples of businesses operating in the city of Livingstone/Victoria Falls, Zambia. It starts by discussing the concept of CSR and provides a hospitality and tourism context by highlighting some of the initiatives and operations developed by many organisations, which are applied to local communities and involve various stakeholders. The benefits of CSR are analysed and linked to brand image, increased profits and better relationships between businesses and local communities. CSR policies are applied to projects like retirement homes, orphanages, sports teams, agricultural projects and others. As a result, both businesses and local communities develop together, bringing an enhanced economic stability and sustainability to the destination.

The development of large language models (LLMs) has significantly enhanced capabilities in AI-powered text generation. The impact of this new technology, which is expected to significantly influence our work and private lives, on document creation is still largely unknown. This article is inspired by the article “What Kind of Science Can Information Science Be” by Buckland (2012).

Buckland’s (2012) considerations about the human’s central role in information science are applied to the question of how the human’s central role in documentation could be affected by the devolvement of LLMs. The Model of Documentation Activity (MoDA) (Donner, 2023) is used as a framework to evaluate the influence of LLM outputs as part of the documentation activity. LLM outputs are placed within the model after an analysis of their potential to be a document from conventional, functional and semiotic points of view.

An advanced and more detailed version of the MoDA, the MoDA2, is presented, which is intended to clarify the potential implications of LLMs on the documentation activity.

This article coins the term “artificially blended testimony” for LLM output as novel data provider along nature and testimony and demonstrates the value of the MoDA2 for exploring the impact of technological advances such as LLMs on the documentation process.

The purpose of this bibliometric study is to report research output and publications on the social determinants of health and health outcomes. This study condenses numerous studies into a single paper, which not only provides insights into the worldwide growth and advancement of the study field but also establishes a research agenda for the future.

Using the Scopus database, a thorough bibliometric study of the worldwide scientific output on social determinants of health from 2000 to 2021 was conducted. With the assistance of VOS viewer and R-based scientometric software, the worldwide development of research on social determinants of health was analysed through performance analysis and network map visualisation.

This study identified the most influential authors, studies, journals and affiliations in the field of social determinants of health, as well as the most co-cited authors and journals, based on a bibliometric analysis of 1,203 research papers retrieved from the Scopus database during the past two decades (2000–2021). Beginning in 2000 with one publication and ending in 2021, 274 articles were viewed online, relevant to the social determinants of health. From 2014 to 2021, publications continue to grow at an accelerating pace.

To the best of the authors’ knowledge, this is one of the first studies to review the social determinants of health and its outcomes. The findings of the chosen studies represent a novel and important addition to the study of social determinants of health. It offers a comprehensive bibliometric analysis of publications, in addition to the identification of vital research trends.

Mental health inequalities based on race and ethnicity in the USA and globally persist despite efforts to address them. The COVID-19 epidemic accentuated these inequalities and demonstrated the extent to which they are linked to social determinants. However, the organizations that are best placed to ameliorate mental health inequalities are often underfunded and under-resourced. Investment strategies that restrict funding for programmatic costs rather than general operating costs often disproportionately impact small organizations that serve communities of color. This study aims to argue that effectively addressing mental health inequalities requires investing in these organizations by applying the lessons learned from implementation science.

This study demonstrates how organizational factors such as leadership, supervision and organizational culture and climate are key to programmatic success and how implementation strategies can target these factors. As promoting health equity is increasingly recognized as a priority outcome for implementation science research, these organizational approaches can inform funders on how to support small organizations that serve marginalized communities, giving them the capacity and flexibility to address mental health inequalities.

This paper applies the findings from implementation science to consider how best to support mental health organizations, particularly those well suited to serving the mental health needs of diverse communities.