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This paper aims to investigate the nonlinear effects of bank regulation stringency on bank lending in 23 sub-Saharan African (SSA) countries over the period 1997–2017.

This study employs the dynamic panel threshold regression (PTR) model, which addresses endogeneity and heterogeneity problems within a nonlinear framework. It also uses indices of entry barriers, mixing of banking and commerce restrictions, activity restrictions and capital regulatory requirements from the updated databases of the World Bank's Bank Regulation and Supervision Surveys as measures of bank regulation.

The linearity test results support the existence of nonlinear effects in the relationship between bank lending and entry barriers or capital regulations in the selected SSA economies. The dynamic PTR estimation results reveal that bank lending responds positively when the stringency of entry barriers is below the threshold of 62.8%. However, once the stringency of entry barriers exceeds that threshold level, bank credit reacts negatively and significantly. By contrast, changes in capital regulation stringency do not affect bank lending, either below or above the obtained threshold value of 76.5%.

These results can help policymakers design bank regulatory measures that will promote the resilience and safety of the banking system but at the same time not bring unintended effects to bank lending.

To the best of the authors’ knowledge, this is the first study to examine the nonlinear effects of bank regulatory measures on bank lending using the dynamic PTR model and SSA context.

The authors aimed to examine how the level of digitalization in Poland and Ukraine affects the contribution of small and medium-sized enterprises (SMEs) to the countries’ gross domestic product (GDP).

The study involved a comparative analysis and statistical modeling of the impact of key economic factors on the contribution of SMEs to Poland’s and Ukraine’s GDP in the 2010–2020 period. The authors used principles of the theory of economic growth and calculated the coefficient of digital competitiveness as a composite indicator consisting of a number of global indices.

The study revealed significant differences between both countries, which can be attributed to a higher level of digitalization in Polish SMEs. The authors used the Polish experience to recommend how to reform Ukraine’s digital economy in postwar recovery.

The contribution of SMEs to Poland’s GDP is higher than that of Ukraine’s because of the higher entrepreneurship rate in the Polish micro and small enterprises (MSEs) sector. The authors found that a unit change in the integrated coefficient of digital competitiveness is related to the greatest change in the contribution of SMEs to the country’s GDP when the other factors in the model equation remain fixed.

The effects of board composition on performance have long been discussed in management research using fractionalization measures. In this study, we propose an alternative measurement approach based on board polarization.

Using an exploratory analysis and applying the polarization measure to German Deutscher Aktienindex (DAX)-, Midcap-DAX (MDAX)- and Small Cap-Index (SDAX)-listed companies, this paper applies the polarization index to examine the relationship between board diversity and performance.

The results show that the polarization concept is well suited to measure principal-agent problems between the members of the management and supervisory boards. We reveal that board polarization is negatively associated with firm performance, as measured by return on investment (ROI).

This exploratory study shows that the measurement of board polarization can be linked to performance differences between companies, which offers promising starting points for further research.

The study aims to estimate the short- and long-run effects of monetary policy on residential property prices in South Africa. Over the past decades, there has been a monetary policy shift, from targeting money supply and exchange rate to inflation. The shifts have affected residential property market dynamics.

The Johansen cointegration approach was used to estimate the effects of changes in monetary policy proxies on residential property prices using quarterly data from 1980 to 2022.

Mortgage finance and economic growth have a significant positive long-run effect on residential property prices. The consumer price index, the inflation targeting framework, interest rates and exchange rates have a significant negative long-run effect on residential property prices. The Granger causality test has depicted that exchange rate significantly influences residential property prices in the short run, and interest rates, inflation targeting framework, gross domestic product, money supply consumer price index and exchange rate can quickly return to equilibrium when they are in disequilibrium.

There are limited arguments whether the inflation targeting monetary policy framework in South Africa has prevented residential property market boom and bust scenarios. The study has found that the implementation of inflation targeting framework has successfully reduced booms in residential property prices in South Africa.

The authors analyse six actively traded VIX Exchange Traded Products (ETPs) including 1x long, −1x inverse and 2x leveraged products. The authors assess their impact on the VIX Futures index benchmark.

Long-run causal relations between daily price movements in ETPs and futures are established, and the impact of rebalancing activity of leveraged and inverse ETPs evidenced through causal relations in the last 30 min of daily trading.

High frequency lead lag relations are observed, demonstrating opportunities for arbitrage, although these tend to be short-lived and only material in times of market dislocation.

The causal relations between VXX and VIX Futures are well established with leads and lags generally found to be short-lived and arbitrage relations holding. The authors go further to capture 1x long, −1x inverse as well as 2x leveraged ETNs and the corresponding ETFs, to give a broad representation across the ETP market. The authors establish causal relations between inverse and leveraged products where causal relations are not yet documented.

The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.

Environmental degradation resulting from human activities may adversely affect human health in multiple ways. Until now, policies aimed at mitigating environmental problems such as climate change, environmental pollution and damage to biodiversity have failed to clearly identify and drive the potential benefits of these policies on health. The conducted study assesses and demonstrates how specific environmental policies and instruments influence perceived human health in order to ensure input for a data-driven decision process.

The study was conducted for the 2004–2020 period in European Union (EU) countries with the use of dynamic panel data modeling. Verification of specific policies' impact on dependent variables allows to indicate this their effectiveness and importance. As a result of the computed dynamic panel data models, it has been confirmed that a number of significant and meaningful relationships between the self-perceived health index and environmental variables can be identified.

There is a strong positive impact of environmental taxation on the health index, and the strength of this relationship causes effects to be observed in the very short term, even the following year. In addition, the development of renewable energy sources (RES) and the elimination of fossil fuels from the energy mix exert positive, although milder, effects on health. The reduction of ammonia emissions from agriculture and reducing noise pollution are other health-supporting factors that have been shown to be statistically valid. Results allow to identify the most efficient policies in the analyzed area in order to introduce those with the best results or a mix of such measures.

The results of the authors' research clearly indicate the health benefits of measures primarily aimed at improving environmental factors, such as environmental taxes in general. The authors have also discovered an unexpected negative impact of an increase in the share of energy taxes in total taxes on the health index. The presented study opens several possibilities for further investigation, especially in the context of the rapidly changing geopolitical environment and global efforts to respond to environmental and health challenges. The authors believe that the outcome of the authors' study may provide new arguments to policymakers pursuing solutions that are not always easily acceptable by the public.

This study aims to map the conceptual structure and evolution of the recent scientific literature published in marketing journals to identify the areas of interest and potential future research directions.

The 100 most influential marketing academic papers published between 2018 and 2022 were identified and scrutinized through a bibliometric analysis.

The findings further upheld the critical role of emerging technologies such as Blockchain in marketing and identified artificial intelligence and live streaming as emerging trends, reinforcing the importance of data-driven marketing in the discipline.

The data collection included only the 100 most cited documents between 2018 and 2022, and data were limited only to Scopus database and restrained to the Scopus-indexed marketing journals. Moreover, documents were selected based on the number of citations. Nevertheless, the data set may still provide significant insight into the marketing field.

Influential authors, papers and journals identified in this study will facilitate future literature searches and scientific dissemination in the field. This study makes an essential contribution to the marketing literature by identifying hot topics and suggesting future research themes. Also, the important role of emerging technologies and the shift of marketing toward a more data-driven approach will have significant practical implications for marketers.

To the best of the authors’ knowledge, this is the first comprehensive study offering a general overview of the leading trends and researchers in marketing state-of-the-art research.

To facilitate the reception and care of discharged patients, streamlining processes at the University Hospital and promoting a seamless transition to continuity of care services post-discharge.

Hospitalised patients undergo the Blaylock risk assessment screening score (BRASS), a screening tool identifying those at risk of complex discharge.

Pre-pandemic, patients with a medium-to-high risk of complex discharge were predominantly discharged to their residence or long-term care facilities. During the pandemic, coinciding with an overall reduction in hospitalisation rates, there was a decrease in patients being discharged to their residence.

The analysis of discharges, with the classification of patients into risk groups, revealed a coherence between the BRASS score and the characteristics of the studied sample. This tool aids physicians in decision-making by identifying the need for a planned discharge in a systematic and organised manner, preventing the loss of crucial information.

This study investigates the relationship between board gender diversity (BGD) and the time to Initial Public Offering (IPO), which stands as an entrepreneurially risky choice, particularly challenging in family firms. We also investigate the moderating role of family ownership dispersion (FOD).

We draw on an integrated theoretical framework bringing together the upper echelons theory and the socio-emotional wealth (SEW) perspective and on hand-collected data on a sample of Italian family IPOs that occurred in the period 2000–2020. We employ ordinary least squares (OLS) regression and alternative model estimations to test our hypotheses.

BGD positively affects the time to IPO, thus, it increases the time required to go public. FOD negatively moderates this relationship. Our findings remain robust with different measures for BGD, FOD, and family business definition as well as with different econometric models.

The article develops literature on family firms and IPO and it enriches the academic debate about gender and IPOs in family firms. It adds to studies addressing the determinants of the time to IPO by incorporating gender diversity and the FOD into the discussion. Finally, it contributes to research on women and outcomes in family firms.