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This paper aims to empirically investigate the dynamic interlinkages among globalization, digitalization and economic development in the top 75 most globalized countries from 2000 to 2019. The selection of the 75 most globalized developing countries is based on the overall scores of the KOF Globalization Index (2021).

The research design is based on secondary data collected from the World Bank (2021), the International Telecommunication Union (2021) and the KOF Globalization Index (2021). The study uses panel unit root tests followed by the panel cointegration techniques. Further, the estimation uses panel fully modified ordinary least squares and panel dynamic ordinary least squares methods.

The empirical results reveal that the effect of globalization on economic development is sensitive to different estimation procedures; in some cases, but not in every case, the effect is positive and significant. However, the positive and significant effect of digitalization on economic development is robust across all estimated models. Long-run equilibrium relationships and bidirectional causalities strongly affirm the nexus among globalization, digitalization and economic development, substantiating the interconnectedness among 75 developing economies.

The study reinstates that the forces of globalization and digitalization will be instrumental in shaping the selected most globalized economies in the long run. Adopting various econometric methodologies takes care of the time-specific and cross-sectional dynamics, as evident in the panel framework considered in this study. The empirical findings truly ascertain the theoretical synergy among the forces of globalization leading to more digitalization and economic development. This makes the empirical interplay highly conducive to framing long-term policies to expand the information communication network in terms of its access and reach.

Empirical literature is the primary source of understanding how policing can effectively reduce criminal activities. Spatial analyses can identify particular effects that can explain and assist in constructing appropriate regional strategies and policies; nevertheless, studies that use spatial regression methods are more limited and can provide a perspective on specific effects in a more disaggregated regional context.

This research aims to conduct a systematic literature review (SLR) to understand the relationship between crime indicators and police production using spatial regression models. We consider a combination of Kitchenham and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocols as a methodological strategy in five bibliographic databases for collecting scientific articles.

The SLR suggests a limited amount of evidence that meets the criteria defined in the research strategy. Several particularities are observed regarding police and criminal production metrics, either in terms of aggregation level, indicator transformations or scope of analysis. A broader time perspective did not necessarily indicate statistical significance compared to models with a single-period sample.

The findings suggest the possibility of expanding efforts by the public sector to provide policing data with the intention of conducting appropriate research using spatial analysis. This step could allow for a more robust integration between the public sector and researchers, strengthening policing strategies, evaluating the effectiveness of public security policies and assisting in the development of strategies for future policy actions.

Limited empirical evidence meets the criteria of spatial regression models with temporal components considering police production and criminality indicators. Constructing an SLR with this scope is an unprecedented contribution to the literature. The discussion can enhance the understanding of approaches for studying the relationship between police efforts and crime prevention.

The adoption and usage of information and communication technology (ICT) has introduced transformation in the tourism arena with ICT applications extensively used in tourism industry. In addition to ICT, an advanced infrastructure is essential for the development of tourism industry. Thus, the goal of present research is to probe the impact of ICT and infrastructure on tourism development (TD) in 28 Asian economies using method of moments panel quantile regression (MM-QR) model introduced by Machado and Silva (2019) applied to a panel data from 2008 to 2020. Empirical findings demonstrate that there is an asymmetric non-linear effect of ICT and infrastructure through all quantile range. This indicates that ICT has negative effect on TD in poor countries while positive impact in rich countries. Negative impact in poor countries may be due to higher establishment cost and information technology (IT) productivity paradox. However, results confirm the importance of ICT and infrastructure in endorsing the development of tourism sector in Asian nations by lessening time and money costs and facilitating travelers.

The paper aims to investigate the effect of GDP growth on crime and to test the hypothesis of nonlinearity. Additionally, we estimate the interaction between GDP and income inequality and examine its impact on the relationship between GDP and homicide rates.

The study utilizes panel data from the Organization for Economic Cooperation and Development (OECD), spanning the period from 2000 to 2018 and estimates dynamic panel GMM models.

We found a nonlinear relationship between GDP and homicide rates, indicating a dual effect of GDP on the occurrence of lethal crimes. Moreover, income inequality conditions the effect of GDP on homicide rates, exerting a significant influence. We conclude that in contexts characterized by high levels of income inequality, GDP growth is more effective in reducing crime, as there is greater potential for improvement.

This paper contributes to the existing literature by providing insights into the complex nonlinearity between economic conditions, income inequality and homicide rates.

The purpose of this paper is to supplement and update previously published articles about artificial intelligence (AI) instruments and operations in banking sectors with the following objectives in mind: to understand the role of AI in banking sectors; to explore the themes and context in this area based on keywords, co-citations and co-words; and to identify future research direction by evaluating the trend and direction of previous research.

This study adopts a semi-inductive approach with the convolution of bibliometrics and literature review. This study used bibliometrics for the identification of literature across multiple databases and systematic literature review on identified articles to explore heterogeneous sectors within AI in banking and finance.

This study contributes a literature-based model that accounts for both the broadly in AI application in banking and finance: predictive modeling in risk assessment and detection; financial decision-making; client service delivery; and emerging FinTech applications of AI and machine learning.

This study is among the few to address the literature of tools and application of AI in banking through mixed-methods approach and produce a synthesized model for the same.

This study aims to provide a framework aligning corporate social responsibility (CSR) initiatives with sustainable development goals (SDGs) 2030, applying the triple bottom line (TBL) approach. The research examines and evaluates the reach of Maharatna Central Public Sector Enterprises’ (CPSE) CSR spending towards sustainability and maps them with SDGs focusing on economic, social and environmental aspects. In addition, state-wise spending for CSR of all eligible Indian companies has been discussed.

The study used secondary data related to CSR spending and disclosure from the annual reports and sustainability reports accessible on the official websites of CPSE, Global Reporting Initiative standards, CSR Guidelines of Department of Public Enterprises and Securities Exchange Board of India, Government of India’s National Guidelines on Responsible Business Conduct (NGRBC) (2018) research papers, financial dailies and websites. The study includes the CPSEs awarded with the status of Maharatna companies under the Guidelines of Maharatna Scheme for CPSEs.

The top CSR initiatives focused on by Maharatna companies were related to poverty, hunger, sanitation and well-being, promotion of education and contribution to the Prime Minister’s National Relief Fund. These initiatives aligned with the top SDGs related to life on land, education and health care, which proved responsible business leadership (RBL) through TBL. The alignment indicates that India is moving towards sustainable development achievements systematically.

The practical consequences can be understood through the CSR spending of Maharatna Public Sector Undertakings towards economic, social and environmental aspects. The spending demonstrates their commitment, which other public and private sector organizations can adopt.

The Government of India’s NGRBC’s guidelines towards inclusive growth and equitable development, addressing environmental concerns, and being responsive to all its stakeholders is a thorough indication of driving the business towards being more responsible. This research has developed a framework aligning CSR and SDG through the TBL approach, which other developing countries can adopt as a model.

There is dearth of research among public sector company’s contribution towards attaining SDGs and demonstrating RBL. This research fulfils this gap. Mapping CSR activities to SDG’s also has not been clearly carried out in previous research, which is a contribution of this study.

The objective of this study is to reevaluate the correlation among pharmaceutical consumption, per capita income, and life expectancy across different age groups (at birth, middle age, and advanced age) within the OECD countries between 1998 and 2018.

We employ a two-step methodology, utilizing two independent approaches. Firstly, we con-duct the Dumitrescu-Hurlin pairwise panel causality test, followed by Machine Learning (ML) experiments employing the Causal Direction from Dependency (D2C) Prediction algorithm and a DeepNet process, thought to deliver robust inferences with respect to the nature, sign, direction, and significance of the causal relationships revealed in the econometric procedure.

Our findings reveal a two-way positive bidirectional causal relationship between GDP and total pharmaceutical sales per capita. This contradicts the conventional notion that health expenditures decrease with economic development due to general health improvements. Furthermore, we observe that GDP per capita positively correlates with life expectancy at birth, 40, and 60, consistently generating positive and statistically significant predictive values. Nonetheless, the value generated by the input life expectancy at 60 on the target income per capita is negative (−61.89%), shedding light on the asymmetric and nonlinear nature of this nexus. Finally, pharmaceutical sales per capita improve life expectancy at birth, 40, and 60, with higher magnitudes compared to those generated by the income input.

These results offer valuable insights into the intricate dynamics between economic development, pharmaceutical consumption, and life expectancy, providing important implications for health policy formulation.

Very few studies shed light on the nature and the direction of the causal relationships that operate among these indicators. Exiting from the standard procedures of cross-country regressions and panel estimations, the present manuscript strives to promote the relevance of using causality tests and Machine Learning (ML) methods on this topic. Therefore, this paper seeks to contribute to the literature in three important ways. First, this is the first study analyzing the long-run interactions among pharmaceutical consumption, per capita income, and life expectancy for the Organization for Economic Co-operation and Development (OECD) area. Second, this research contrasts with previous ones as it employs a complete causality testing framework able to depict causality flows among multiple variables (Dumitrescu-Hurlin causality tests). Third, this study displays a last competitive edge as the panel data procedures are complemented with an advanced data testing method derived from AI. Indeed, using an ML experiment (i.e. Causal Direction from Dependency, D2C and algorithm) it is believed to deliver robust inferences regarding the nature and the direction of the causality. All in all, the present paper is believed to represent a fruitful methodological research orientation. Coupled with accurate data, this seeks to complement the literature with novel evidence and inclusive knowledge on this topic. Finally, to bring accurate results, data cover the most recent and available period for 22 OECD countries: from 1998 to 2018.

The article aims to establish whether the degree of aversion to inflation and the responsiveness to deviations from potential output have changed over time.

This paper assesses time variation in monetary policy rules by applying a time-varying parameter generalised methods of moments (TVP-GMM) framework.

Using monthly data until December 2022 for five inflation targeting countries (the UK, Canada, Australia, New Zealand, Sweden) and five countries with alternative monetary regimes (the US, Japan, Denmark, the Euro Area, Switzerland), we find that monetary policy has become more averse to inflation and more responsive to the output gap in both sets of countries over time. In particular, there has been a clear shift in inflation targeting countries towards a more hawkish stance on inflation since the adoption of this regime and a greater response to both inflation and the output gap in most countries after the global financial crisis, which indicates a stronger reliance on monetary rules to stabilise the economy in recent years. It also appears that inflation targeting countries pay greater attention to the exchange rate pass-through channel when setting interest rates. Finally, monetary surprises do not seem to be an important determinant of the evolution over time of the Taylor rule parameters, which suggests a high degree of monetary policy transparency in the countries under examination.

It provides new evidence on changes over time in monetary policy rules.

The primary purpose of this research is to identify and compare the multifractal behavior of different sectors during these crises and analyze their implications on market efficiency.

We used multifractal detrended fluctuation analysis (MF-DFA) to analyze stock returns from various sectors of the Moscow Stock Exchange (MOEX) in between two significant periods. The COVID-19 pandemic (January 1, 2020, to December 31, 2021) and the Russia–Ukraine conflict (RUC) (January 1, 2022, to June 30, 2023). This method witnesses multifractality in financial time series data and tests the persistency and efficiency levels of each sector to provide meaningful insights.

Results showcased persistent multifractal behavior across all sectors in between the COVID-19 pandemic and the RUC, spotting heightened arbitrage opportunities in the MOEX. The pandemic reported a greater speculative behavior, with the telecommunication and oil and gas sectors exhibiting reduced efficiency, recommending abnormal return potential. In contrast, financials and metals and mining sectors displayed increased efficiency, witnessing strong economic performance. Findings may enhance understanding of market dynamics during crises and provide strategic insights for the MOEX’s investors.

Understanding the multifractal properties and efficiency of different sectors during crisis periods is of paramount importance for investors and policymakers. The identified arbitrage opportunities and efficiency variations can aid investors in optimizing their investment strategies during such critical market conditions. Policymakers can also leverage these insights to implement measures that bolster economic stability and development during crisis periods.

This research contributes to the existing body of knowledge by providing a comprehensive analysis of multifractal properties and efficiency in the context of the MOEX during two major crises. The application of MF-DFA to sectoral stock returns during these events adds originality to the study. The findings offer valuable implications for practitioners, researchers and policymakers seeking to navigate financial markets during turbulent times and enhance overall market resilience.

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.