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This study aimed to assess the potential of the Clinical Dementia Rating (CDR) Scale in the prognosis of dementia in elderly subjects.

Dementia staging severity is clinically an essential task, so the authors used machine learning (ML) on the magnetic resonance imaging (MRI) features to locate and study the impact of various MR readings onto the classification of demented and nondemented patients. The authors used cross-sectional MRI data in this study. The designed ML approach established the role of CDR in the prognosis of inflicted and normal patients. Moreover, the pattern analysis indicated CDR as a strong cohort amongst the various attributes, with CDR to have a significant value of p < 0.01. The authors employed 20 ML classifiers.

The mean prediction accuracy varied with the various ML classifier used, with the bagging classifier (random forest as a base estimator) achieving the highest (93.67%). A series of ML analyses demonstrated that the model including the CDR score had better prediction accuracy and other related performance metrics.

The results suggest that the CDR score, a simple clinical measure, can be used in real community settings. It can be used to predict dementia progression with ML modeling.

Daily commuting trips of higher education (HE) students account for a large proportion of the carbon footprint of a HE institution. Considerations of students underlying their choice of travel mode and their decision to make the trip to campus or to study online are explored as a necessary first step for finding an optimal balance between online and on-campus learning from both a sustainability and an educational perspective.

Focus group conversations were held with student groups from different study programmes of a university of applied sciences in the Netherlands.

Dutch students’ travel mode choices seem to depend on measures regulating travel demand such as a free public transport card and high parking costs. The findings indicate that students make reasoned choices about making a trip to campus. These choices depend on considerations about their schedule, type, lecturer and content of a course, social norms and their own perceived behavioural control. Alternative online options can provide students with more flexibility to make choices adapted to their needs.

While these findings are useful for sustainable and educational reasons, they also seem helpful in times of COVID-19 which calls for a re-design of curricula to allow for blended forms of online and on-campus learning.

To the best of the authors’ knowledge, this paper is one of the first studies looking at students’ considerations when deciding whether to travel to campus to learn or stay at home learning online.

The purpose of the current application paper is to integrate previous literature with recent results from practitioners on effective practices for utilizing small groups in the leadership classroom.

We use these integrated findings to innovate practice on maximizing the role of undergraduate teaching assistants (UTAs) by matching their tasks to signature pedagogies in leadership education.

The integrated findings revealed three practice themes: (1) match UTAs to pedagogical approach, (2) create small groups by design and (3) providing training and reflection practice for UTAs.

In sum, leadership education can and should challenge historical practices in higher education whereby UTAs are used purely for grading and course logistics management.

The increased interest among academicians to explore more about tax management behavior is evident in the literature on corporate tax avoidance. This paper aims to illustrate the multiple aspects that influence the tax avoidance behavior of corporations and its impacts through the systematic review method.

This study used “Tax Avoidance” OR “Tax Aggressiveness” OR “Tax Planning” as search strings to extract the relevant literature from the Scopus database. This study is a comprehensive analysis of existing literature on corporate tax avoidance behavior. Further, the keyword network analysis has been used to find out the most explored and dry research areas related to corporate tax avoidance behavior using VOSviewer software.

The study finds that taxation decision is an important managerial decision. Managers adopt tax avoidance tactics to boost postax profits to meet the shareholders’ expectations, particularly of risk-averse shareholders, and sometimes for their benefit also. With this, this study also finds that firms’ characteristics, political connections and corporate social responsibility activities also impact taxation decisions. In addition, the study identifies that tax-avoiding behavior has a contradictory impact on firm value, market growth and corporate transparency disclosure decisions.

The study assists the researchers by providing a brief overview of tax avoidance behavior, for corporates in understanding the implications of tax avoidance, and for policymakers to fix the taxation loopholes and bring necessary tax reforms.

This study adds to the existing literature by providing a thorough overview of theories, determinants and outcomes of corporate tax avoidance behavior.

Tax evasion and tax avoidance consistently strip national budgets of tremendous financial resources. Consequently, the discussion on tax fraud remains topical and a moving target subject matter. Various antecedents and moderators of tax fraud have been investigated over the years, yet the literature dedicated to the linkage between corporate social responsibility (CSR) and tax practices exhibited ambiguous results. In this respect, the purpose of this study is to present the results of an investigation into the nonfinancial factors affecting tax fraud and the moderating effect of CSR-related behaviors.

Structural equation modeling (SEM) was applied in a multivariate statistical analysis technique to analyze structural relationships. The measurement and structural models were evaluated using component-based partial least squares (PLS), a rigorous statistical instrument. The opportunity to use PLS-SEM is supported by the advancement of models comprising both reflective and formative constructs as in the present case.

Data collected from a sample of 290 respondents from Romania confirmed that social and ethical factors significantly impact tax fraud and CSR-related behaviors. In addition, the latter plays a moderating effect between nonfinancial factors and tax fraud.

The research sample is country-centric (i.e. subjects come from Romania) while the questionnaire-based survey relies on self-reported measures.

The paper adds new evidence to the extant knowledge and points to theoretical and managerial implications.

This study aims to describe the compounding factors in a complex emergency, which exacerbate a cholera epidemic among vulnerable populations due to supply chain disruptions. Basic needs such as food, medicine, water, sanitation and hygiene commodities are critical to reduce the incidence rate of cholera and control the spread of infection. Conflicts cause damage to infrastructure, displace vulnerable populations and restrict the flow of goods from both commercial and humanitarian organizations. This study assesses the underlying internal and external factors that either aggravate or mitigate the risk of a cholera outbreak in such settings, using Yemen as a case study.

This study adopts a system dynamics methodology to analyze factors that influence cholera outbreaks in the context of the Yemeni Civil War. A causal loop diagram with multiple components was constructed to represent the complexities of humanitarian situations that require critical decision-making. The model was built using data from humanitarian organizations, non-governmental organizations and practitioners, along with literature from academic sources. Variables in the model were confirmed through semi-structured interviews with a field expert.

Compounding factors that influenced the cholera outbreak in Yemen are visualized in a causal loop diagram, which can improve the understanding of relationships where numerous uncertainties exist. A strong link exists between humanitarian response and the level of infrastructure development in a country. Supply chains are affected by constraints deriving from the Yemeni conflict, further inhibiting the use of infrastructure, which limits access to basic goods and services. Aligning long-term development objectives with short-term humanitarian response efforts can create more flexible modes of assistance to prevent and control future outbreaks.

The model focuses on the qualitative aspects of system dynamics to visualize the logistics and supply chain-related constraints that impact cholera prevention, treatment and control through humanitarian interventions. The resulting causal loop diagram is bounded by the Yemen context; thus, an extension of the model adapted for other contexts is recommended for further study.

This study presents a systematic view of dynamic factors existing in complex emergencies that have cause-and-effect relationships. Several models of cholera outbreaks have been used in previous studies, primarily focusing on the modes and mechanisms of transmission throughout a population. However, such models typically do not include other internal and external factors that influence the population and context at the site of an outbreak. This model incorporates those factors from a logistics perspective to address the distribution of in-kind goods and cash and voucher assistance.

This study has been aligned with six of the United Nations Sustainable Development Goals (SDGs), using their associated targets in the model as variables that influence the cholera incidence rate. Recognizing that the SDGs are interlinked, as are the dynamic factors in complex humanitarian emergencies, the authors have chosen to take an interdisciplinary approach to consider social, economic and environmental factors that may be impacted by this research.

This paper provides an insight into the underlying inter-relations of internal and external factors present in the context of a cholera outbreak in a complex crisis. Supply chains for food; water, sanitation and hygiene; and health products are crucial to help prevent, control and treat an outbreak. The model exposes vulnerabilities in the supply chain, which may offer guidance for decision makers to improve resilience, reduce disruptions and decrease the severity of cholera outbreaks.

This study aims to identify factors affecting tax avoidance and tax evasion in Bangladesh and propose a future research agenda.

This paper reviewed 423 articles published between 2010 and 2023 using a systematic literature review (SLR) approach.

The review classified the factors into three categories, namely individual taxpayers, corporate taxpayers and tax administration. Income level, tax penalty, tax morale, inefficient tax return system and tax assessment process are associated with the individual’s tax avoidance and tax evasion activities. Profitability, corporate governance and financial restrictions are key factors influencing corporate taxpayers’ involvement in tax avoidance and tax evasion. Factors related to tax administration include lack of social interaction, distrust of national officials, complexities of policies, politicisation of tax authority, lack of political stability, incompetent auditing, insufficient recording, lack of administrative cooperation, lack of accountability, insufficient counselling and compromising in tax prosecution cases.

This paper provides tax regulators with insights to improve regulations and lessen tax avoidance and tax evasion activities.

This paper is the first attempt to provide guidance for academics when examining tax avoidance and tax evasion in Bangladesh.

Crowdfunding is an increasingly popular channel for project fundraising for entrepreneurial ventures. Such efforts require fundraisers to develop and manage a crowdfunding campaign over a period of time and several stages. Thus, the authors aim to identify the stages fundraisers go through in their crowdfunding campaign process and how their engagement evolves throughout this process.

Following a multiple case study research design analysing six successful campaigns, the current study suggests a taxonomy of stages the fundraisers go through in their crowdfunding campaign management process while identifying the types of engagement displayed and their relative intensity at each of these stages.

The study proposes a five-stage process framework (pre-launch, launch, mid-campaign, conclusion and post-campaign), accompanied by a series of propositions outlining the relative intensity of different types of engagement throughout this process. The authors show that engagement levels appear with high intensity at pre-launch, and to a lesser degree also at the post-launch stage while showing low intensity at the stages in between them. More specifically, cognitive and behavioural engagement are most prominent at the pre- and post-launch stages. Emotional engagement is highest during the launch, mid-launch and conclusion stages. And social engagement maintains moderate levels of intensity throughout the process.

This study focuses on the campaign process using engagement theory, thus identifying the differing engagement patterns throughout the dynamic crowdfunding campaign management process, not just in one part.