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Institute for Economics and Peace (IEP) every year produces peace setting scores of many countries over the globe. The peace index score (PIS) and its ranking are fabricated by involvement of numerous recognised variables. The internal and external violence factors and their intensity regulate the strength of such scores. The present study deliberates such peace scores and rank of the countries in terms of regional variation and income class specifications. The study uses alteration of rank over three consecutive years and the growth of the PIS to elucidate the disparities over the region and income classification. The study recognised that income classification of countries smoothly elucidates the rank differences in terms of peace scores, but regional variation wise enlightenment remains misnomer. Further, this study contemplates four violence indicators and their growth over three years to describe the non-conformities of peace score between the countries. Studies acknowledged that inter-correlation between the peace growth scores and also between the growth of violence indicators is the significant factor to comprehend the peace score behaviour. Over the years, studies confirm that high-income countries are able to improve their PIS though there has large levels of inconsistency among countries. Among the regional specification, studies perceive that countries around the globe improved their peace score during 2018–2019, rather than 2019–2020.

Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.

Introduction: This chapter examines the impact of Financial Technology (Fintech) on Environmental, Social, and Governance (ESG) goals to promote a sustainable financial system. Digital payment platforms, blockchain applications, and AI-powered analytics have revolutionised the financial landscape in recent years. These advancements have made integrating ESG principles into investment decisions and business practices easier.

Purpose: The main aim of this chapter is to analyse the connections and possibilities that Fintech offers to achieve ESG goals. Understanding how Fintech can facilitate sustainable finance practices is crucial for promoting investment in Fintech.

Methodology: A series of indexes have been examined, including the Global FinTech Index (GFI) in Global and Regional Rank, the Global Sustainable Competitiveness Index, and performing the Green Growth Index, the Green Economic Opportunity Index, the Global Green Finance Index (GGFI), and the Financial Inclusion Index.

Findings: Through comparative analysis, it can be concluded that the countries with the highest rankings are Sweden, Finland, Denmark, Switzerland, and Germany. Sweden ranks highly in the GFI. These results show that these countries rank highly in achieving ESG objectives. Balkan countries, specifically Albania, Bosnia and Herzegovina, and Montenegro, have the weakest results compared to other countries. Policymakers can benefit from the study’s findings to design better regulations and frameworks that promote responsible fintech practices and foster sustainable finance.

Practical Implications: Regulators and agencies responsible for measuring fintech and ESG should strive to align the indexes associated with these two domains as closely as possible. In addition, businesses can utilise the findings of this study to increase awareness about the diverse solutions that fintech offers to achieve the objectives of ESG.

Introduction: Today the ability of nations to develop and implement innovations is core for their international competitiveness. Ukraine is striving for innovation progress; however, its innovation performance is relatively low. The research problem is to find the bottlenecks, affecting Ukraine’s innovation capability.

Purpose: This study aims to research the national innovation capability profiles, based on cluster analysis, to develop an understanding of drivers and threats for the innovation capability of Ukraine.

Need of the study: The knowledge-based economy, which had already turned into one of the most efficient developmental models of the 21st century, became a key driver of international competitiveness for the leading developed countries due to their progressive structural shifts towards the growth of high-technology manufacturing and knowledge-intensive sectors. These trends are significant to capture for the sake of increasing the innovation capability of the economy of Ukraine.

Methodology: The study is based on the K-means clustering method, which is employed for identifying 10 country clusters based on the indicators of their R&D and innovation activities, which allowed us to assess the innovation capability of Ukraine in comparison with 140 countries of the world. Data selection and normalisation were based on the 2019 Global Competitiveness Report indicators.

Findings: The study showed that Ukraine’s innovation capability problems are typical for most developing countries and are prevalently connected to low R&D expenditures, patent applications, and international co-invention activities. Most countries, except for the technologically developed ones, follow the so-called ‘passive technological learning’ strategies, which usually result in low economic productivity.

Practical implications: Several innovation policy implications have been developed for the government of Ukraine based on the cluster analysis results and accounting for the problems of the national innovation system (NIS).

The aim of this chapter is to draw attention to the changes that have taken place in Czechia in the last 20 years in the field of foreign trade, focussing on the key milestones of 2002, 2012 and 2022. The chapter also explains the important link between the performance of foreign trade and economic growth; this link has its support in theory, and above all in empiricism. The importance of foreign trade for economic growth is key, especially from the point of view of changes in the territorial and commodity structure, which saw several important changes in the observed period 2002–2022, so we can relevantly explain the effects on the economic growth of Czechia. However, the chapter finds a connection with yet another economic category, which is competitiveness. The method of measuring and subsequent ranking of competitiveness is also of utmost importance. If the economy is to be competitive, it must have its own strategy, and this directly concerns the key instruments of pro-export policy.

A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.

Internationalisation and academic mobility have long been integral parts, although serving different purposes in the higher education industry. Internationalisation has played a crucial role in facilitating academic exchange, knowledge sharing, research partnerships and collaborative innovation. However, the rise of neoliberalism has introduced the market forces of global capitalism that have significantly impacted higher education worldwide – invading the sector with neoliberal market values. This chapter aims to explore the impact of neoliberalism on the internationalisation of higher education in Africa, with a specific focus on trends in international student mobility. The chapter argues that the influence of neoliberalism on international mobility extends beyond market dynamics, encompassing discussions on hegemony within international knowledge systems, where African countries and institutions often find themselves marginalised. The study relies on published materials and publicly available statistical data from both governmental and non-governmental organisations. By examining the interplay between neoliberalism and the process of internationalisation in higher education, this chapter sheds light on the intricate and multifaceted aspects of both concepts, as well as their practical implications for international student mobility. Moreover, the chapter reflects on the implications of neoliberal entanglements for the prospects of internationalisation in African higher education.

The purpose of this chapter is to highlight how universities got into the predicament in which they currently find themselves in, or somewhat planned to be in, in the 2020s. The historical account outlines the purpose of higher education and who it was for throughout the last few centuries, before a more in-depth analysis of the last few decades will highlight how, and why, neoliberal and subsequently managerial aspects of leadership and performance metrics crept into universities before an analysis of the last 5–10 years, including the onset and consequences of COVID, will demonstrate that many ‘COVID recovery plans’ around staff cuts and course reforms were already in place before COVID, but it was COVID that allowed these plans in most cases to be escalated.