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This study aims to evaluate the advantages and disadvantages of auditor mandatory suspicious activity reporting versus the exercise of professional judgement in the anti-money laundering regimes of the UK and the USA.

The research draws upon the following sources. Firstly, statistics provided by the UK National Crime Agency, 2019 (NCA) regarding suspicious activity report (SAR) filing rates. Secondly, anti-money laundering legislation in the USA and UK. Thirdly, statements made in the political domain in the USA, particularly those which raised constitutional concerns during the progress of the Patriot Act 2001. Finally, statements and recommendations by a UK Parliamentary Commission enquiring into the effectiveness of the suspicious activity reporting regime.

The UK reporting regime does not accommodate professional judgement, resulting in the filing of SARs with limited intelligence value. This contrasts with discretionary reporting in the USA: voluntary reporting guides and influences auditor behaviour rather than mandating it. Defensive filing by UK auditors (defence to anti-money launderings [DAMLs]) has increased in recent years but the number of SARs filed has declined.

The study evaluates auditor behavioural responses to legislative regimes which mandate or alternatively accommodate discretion in the reporting suspicion of money laundering. Consideration of constitutional and judicial activism in this context is a novel contribution to the literature. For its theoretical framework the study uses Foucault’s concept of discipline of the self to evaluate auditor behaviour under both regimes.

The information used in the case study was obtained from secondary sources, such as internal documents, reports, news, and organization websites. Three of the four authors played a hands-on role in the case.

The COVID-19 pandemic exacerbated the global challenge of hiring and retaining health-care workers. To address its own challenges, Mayo Clinic decided to fundamentally transform its 30-year-old tuition assistance program: from a model centered on the premise that tuition assistance was an employee benefit for professional development purposes, to one that was more driven to meet the business needs of the employer by preparing internal talent for important roles throughout the institution. Herein, this case study first describes how the COVID-19 pandemic impacted health-care organizations like Mayo Clinic. Next, this study provides details on the original employee tuition assistance program, and then, focuses on the reasons for its need to be changed. Afterward, this study introduces the new tuition assistance programs. Finally, this study follows with examples of how both Mayo Clinic and its employees navigated through initial challenges, such as resistance to change and lack of engagement. In sum, this case study provides critical insight into designing workforce education programs that provide professional development for meeting the workforce needs of the organization.

This case can be used as teaching material in relevant undergraduate- and MBA-level courses, such as human resource management, human resource development and compensation and benefits. This case allows students to critically analyze workforce education programs (e.g. tuition assistance programs) and to plan how to strategically align those with the workforce needs of the organization.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

This paper aims to explore the ambiguity and limitations of measuring firm-level multinationality (FLM) using theoretical and empirical comparisons of existing methods. The paper puts forward a list of five key aspects that collectively serve as a tool for researchers to select the most appropriate method for future research and as a basis for the future development of methods.

Firstly, the author reviews existing methods of measuring FLM and consolidates findings into five key aspects. Secondly, the author uses the aspects to compare existing methods theoretically, and subsequently, the author groups them into three distinct streams. Thirdly, the author compares existing methods across a sample of the 35 largest European MNEs by sales in 2020 to identify and demonstrate the ambiguity and limitations of these methods.

The author identifies the five key aspects of measuring FLM: framework, aggregation, segmentation, metrics and indicators. Using empirical comparison, the author empirically confirms the limitations highlighted in the literature and shows the differences and inconsistencies among methods, which cause confusion rather than clarity in the extant literature. Additionally, the author emphasises that three distinct streams further drive the debate on the regional/global nature and present further limitations of methods not mentioned in the literature to date.

This paper provides the most comprehensive review of the existing literature on FLM, resulting in five novel aspects of measuring FLM. The analysis of a sample of 35 European firms demonstrates and identifies the ambiguity and limitations of FLM-measuring methods.

This study aims to investigate whether socially responsible businesses with corporate social expenditure are less prone to engaging in tax avoidance. The study also examines whether political connections moderate the association between corporate social responsibility (CSR) and tax avoidance.

The study uses ordinary least squares to analyse the panel data of all 30 listed banks on the Dhaka Stock Exchange covering 2012 to 2020. The study uses a set of alternative variables to check the robustness of the findings.

Confirming the corporate culture theory, the study findings indicate that the higher the firms’ CSR expenditure, the lower the tax avoidance. Contrarily, the moderating effect of political connection weakens the role of CSR in tax avoidance, implying that political relation makes the firms socially irresponsible. Besides, the findings document that firms with strong political connections are more likely to be tax aggressive by weakening the role of CSR. The findings imply that firms with weaker political connections are more socially responsible than firms with strong political ties.

The study provides the bank management and regulatory bodies valuable insights to take necessary actions so that they can easily monitor whether the banks follow their instructions regarding CSR and tax payments. As the politicians make the firm socially irresponsible, the regulatory bodies and bank management should not keep them or their relatives on the board.

The study contributes to the CSR and tax avoidance literature considering the moderating role of political connections in Bangladesh banking sector.

Exploring how racism continues to persist throughout public and nonprofit organizations is central to undoing persistent society-wide injustices in the United States and around the globe. The authors provide two cases for identifying and understanding the ways in which philanthropy’s whiteness does harm to K–12 students and communities of color.

In this article, the authors draw on critical race theory and critical whiteness studies, specifically Cheryl Harris' work to expose the whiteness of philanthropy, not as a racial identity, but in the way that philanthropy is performed. The authors characterize one of the property functions of whiteness, the right to exclude, as working through two mechanisms: neoliberal exclusion and overt exclusion. Drawing on this construction of the right to exclude, the authors present two cases: the Bill & Melinda Gates Foundation and the City Fund.

Whether intentional or not, the Gates Foundation and the City Fund each exclude communities of color in several ways: from changes to schools and districts, parents' experiences navigating school enrollment due to these changes, to academic assessments and political lobbying.

These cases provide a way for researchers and practitioners to see how organizations in real time reify the extant racial hierarchy so as to disrupt such organizational processes and practices for racial justice.