This paper analyses the competition and concetration of insurance markets in Bosnia and Herzegovina, Montenegro, Serbia, and North Macedonia. We measure market structure using the Herfindahl–Hirschman Index (HHI) and market development using premiums per capita (EUR) and the share of premiums in GDP. Country-specific linear time trends are estimated with OLS and heteroskedasticity and autocorrelation-consistent standard errors, complemented with rank-based Kendall and Spearman tests designed for short series and potential nonlinearity. We document statistically significant deconcentration in Montenegro and Serbia, a significant increase in concentration in Bosnia and Herzegovina, and an inconclusive pattern for North Macedonia given the limited availability of data. In parallel, premiums per capita trend upward across all markets, with typical annual growth ranging from roughly 9% (Bosnia and Herzegovina) to 14% (Serbia). Taken together, the results support the hypothesis that higher competition is not directly or uniformly associated with market development over the 2019–2024 window. Policy recommendations emphasize proportionate entry facilitation, conduct-focused supervision, and investment in statistical capacity for line-of-business series and longer time frames.
The impact of foreign direct investment (FDI) inflows and outflows on domestic investment in Botswana was examined using data for the period from 1990 to 2022. The study was motivated by Botswana’s efforts to attract FDI in support of economic diversification. The question this study sought to answer was: “Does the liberalisation of foreign investment outflows and inflows in Botswana support domestic investment?” The study employed the non-linear autoregressive distributed lag (NARDL) approach to assess whether foreign direct investment complements or the substitutes domestic investment in Botswana. The study found that positive shocks to foreign direct investment inflows complement domestic investment in the short run but substitute it in the long run, while negative shocks to foreign direct investment inflows are insignificant across both time horizons. Positive shocks to foreign direct investment outflows were found to complement domestic investment in the short run but substitute it in the long run. Conversely, negative shocks to foreign direct investment outflows lead to an increase in domestic investment in the long run, although they are insignificant in the short run. Policy implications are also discussed.
The paper studies the impact of entrepreneurial activity on three components of sustainable development: economic, social and environmental. Three distinct variables, i.e. new businesses, established businesses, and ambitious entrepreneurs, represent entrepreneurial activity. Variables, GDP growth rate, modified human development index and carbon dioxide emissions are used to observe sustainable development. The aim of the paper is to determine whether entrepreneurship affects sustainable development and, if yes, in what form. There are three econometric panel models created for research purposes. A panel analysis was performed on a sample of 35 countries over ten years. The results indicated a contradictory impact of the variables used to measure the level of entrepreneurial activity, while none of them showed an effect on overall sustainable development.
The paper will examine the economic impact of Local Self-Government Units (LGUs) on the improvement of air quality and environmental protection in cities and municipalities (one city and one municipality from each district, except Belgrade) in the Republic of Serbia. One of the factors affecting life in cities in the Republic of Serbia is air pollution, which can have a negative impact both on the health of residents living there, as well as on those who come for business or tourism. The subject of research in this paper is general data on air quality in LGUs (cities and municipalities) in the Republic of Serbia, as well as planning and the use of funds by competent LGU institutions for establishing and managing air quality. The aim of this paper is to examine whether the competent LGU authorities apply the planning and legislative framework as a basis for effective and efficient air quality management, implement measures and activities to improve air quality, and whether they collect and allocate funds for this purpose. A survey conducted on a sample of 49 LGUs has shown that most of them do not manage air quality adequately. The research has shown that LGUs insufficiently plan and allocate financial resources in their budgets for maintaining air quality. From the accounting perspective, financial resources that LGUs generate from environmental pollution charges and fees for environmental protection and improvement belong to the budget of the local self-government unit and are important in the planning and incurring expenses (costs) for air quality management.
This study investigates how the adoption of artificial intelligence (AI) is associated with workforce upskilling within small and medium-sized enterprises (SMEs) in Oyo State, Nigeria. While AI holds considerable transformative promise for human resource development, a critical knowledge gap persists regarding its regional implementation dynamics, particularly within the context of developing economies. Drawing on an integrated five-theory framework – the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), the Technology-Organisation-Environment (TOE) framework, Human Capital Theory, and Sociotechnical Systems Theory – this study develops and tests a conceptual model linking three theoretically grounded AI adoption dimensions (organisational integration, AI training programmes, and data-driven decision-making support) to employee skill enhancement outcomes. Employing a quantitative cross-sectional survey design, the structured questionnaires were administered to 135 respondents comprising HR professionals, SME operators, and employees drawn from approximately 72 SMEs across diverse industry sectors in Ibadan Metropolis. Results indicate that AI integration in HR upskilling practices remains largely nascent (M = 2.116). Nevertheless, Pearson correlation and regression analyses revealed significant positive associations between key AI adoption dimensions and employee skill enhancement (R = 0.750, R2 = 0.562, p < 0.001). These associations are interpreted as correlational rather than causal given the cross-sectional design. Prominent barriers include infrastructural inadequacies, educational deficiencies, policy gaps, and socio-cultural resistance. The study concludes with actionable policy and practice recommendations, and acknowledges methodological limitations including common method bias risk, absence of formal EFA/CFA, and cross-sectional design constraints.
Mobile payment technology allows digital transactions via smartphones and tablets using methods like NFC, QR codes, and payment apps. This innovation enables consumers to purchase goods and services without physical cards or cash. The global mobile payment market, valued at $2.98 trillion in 2023, is expected to grow to $27.81 trillion by 2032, changing how customers engage with businesses and manage finances. In Nigeria and Kenya, mobile phones serve as vital tools for financial services, e-commerce, and entertainment. This study aims to compare the adoption of digital mobile payments and their impact on economic growth in these countries. It uses quarterly data from Q1 2010 to Q4 2024 from the Central Banks of Kenya and Nigeria, employing the Auto-Regressive Distributed Lag (ARDL) model for analysis. Unit root tests showed variables were integrated of I(0) and I(1), and co-integration tests confirmed long-term relationships. Findings reveal that mobile money payments significantly influence economic growth in both nations, with mixed short-term effects and a positive long-term correlation. The study recommends collaboration among regulators, mobile network operators, fintech firms, and banks to enhance mobile financial services in both countries.
This study develops a hybrid forecasting model for the USD/DZD exchange rate by combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Long Short-Term Memory (LSTM) networks to address high volatility and complex temporal dependencies in currency markets. Using 310 monthly observations, the CEEMDAN procedure decomposes the series into five frequency components and a residual, which are modeled by component-specific LSTM networks. The proposed CEEMDAN-LSTM model achieved the lowest forecast errors among the tested models, with a MAPE of 0.4782%, outperforming traditional LSTM and SVM benchmarks. The 12-month forecast suggests relative exchange rate stability, with a slight decline of about 0.48%. The results indicate that decomposing the original series before LSTM modeling improves predictive accuracy by separating short-term noise from medium- and long-term dynamics. The proposed framework may support exchange-rate risk management, hedging decisions, and short- to medium-term planning in emerging-market settings.
This paper develops an integrated empirical workflow, referred to as the Interactive Panel Data Framework (IPDF), that systematically combines established panel data methods, such as interaction terms, threshold analysis, and marginal effect computation, within a unified estimation and inference strategy. Rather than proposing a new estimator, the IPDF provides a coherent analytical protocol for jointly evaluating regime-dependent, interaction-driven relationships in macro-panel setting. Using a balanced panel of emerging economies over the period 1980–2023, the study combines interaction terms, dynamic specifications, and nonlinear mechanisms within a unified empirical structure. Monte Carlo simulations and empirical estimations support the robustness of the proposed framework, while homogeneity tests and threshold analysis reveal substantial country-specific heterogeneity. The empirical results indicate statistically significant threshold and conditional marginal effects, showing that the impact of inflation and exchange rates on economic growth varies across regimes and economic conditions. Moreover, the identified interaction effects highlight the importance of jointly evaluating macroeconomic policy variables rather than analysing them in isolation. By integrating interaction effects, marginal responses, and threshold dynamics within a single panel data framework, this study contributes a coherent and policy-relevant empirical approach for analysing nonlinear and regime-dependent macroeconomic relationships in emerging economies.
This paper provides an econometric assessment of the macroeconomic contributions of tourism in Republic of Srpska. The analysis is based on the Tourism-Led Economic Growth (TLEG) hypothesis and examines the elasticity of gross value added in the services and agriculture sectors in relation to tourist arrivals, as well as the long-term relationship between tourism and GDP growth. The results suggest that tourism positively influences economic development, particularly through its effects on service-oriented and agricultural activities. Despite the limitations in the availability and reliability of official data, the findings support the recognition of tourism as a strategic sector. The paper highlights the need for improved statistical infrastructure, greater investment and the strategic development of the tourism sector to enhance its growth and competitiveness potential.
This study examines the nexus between economic growth and carbon dioxide (CO2) emissions in East African Community (EAC) countries, with a focus on the role of renewable energy consumption. As EAC countries undergo rapid industrialisation and urbanisation, understanding the impact of economic growth on emissions is critical for shaping sustainable development policies. Using a panel cointegration approach, the study applies the environmental Kuznets curve (EKC) hypothesis to data from six EAC countries, namely, the Democratic Republic of the Congo, Burundi, Rwanda, Kenya, Uganda and Tanzania, for the period from 1990 to 2022. The panel autoregressive distributed lag (ARDL) model is employed, with the pooled mean group (PMG) estimator used to analyse both long-run and short-run dynamics. The results reveal a U-shaped relationship between economic growth and CO2 emissions, challenging the traditional inverted U-shaped EKC hypothesis. The findings suggest that while the early stages of economic growth reduce emissions, emissions begin to rise again beyond a certain income threshold, indicating a potential overdevelopment phase. Renewable energy consumption is found to significantly reduce CO2 emissions; however, its economic benefits are constrained by infrastructural and policy challenges. This study contributes to existing literature by integrating renewable energy into the EKC framework and offers valuable insights for policymakers seeking to balance economic growth with environmental sustainability. The findings emphasise the need for targeted policies to promote clean energy adoption, low-carbon industrialisation and stronger environmental governance across EAC countries.
Risk management procedures in financial institutions around the world have been significantly altered by artificial intelligence (AI). However, little is known about the perceived risks of implementing AI, especially in developing nations such as South Africa. The aim of this study is to assess, from a multidimensional perspective, the perceived risks of AI adoption by employees in South African financial institutions. This study employs a mixed-methods approach, using a purposive and snowball sample of 90 survey respondents and semi-structured interviewees from several South African financial institutions. The study revealed a broad spectrum of concerns ranging from AI-induced unemployment to cybercrime vulnerabilities. The analysis provides layered insights into how different departments, including Risk Management, IT, and Operations Management, uniquely perceive and manage AI-related challenges. This study underscores the need for personalised risk management strategies that meet unique departmental concerns, as well as the importance of strategic planning in the integration of AI technology by financial institutions to maximise potential while limiting associated risks. It adds to the growing body of knowledge on AI adoption in emerging markets by providing practical information to practitioners and policymakers.
This study examined the effects of agricultural finance and financial development on the economic growth of the SSA from 2000 to 2021. The study employed the panel ARDL regression models and the panel VAR-based Granger causality test as tools for data analysis. The study revealed that capital accumulation impacts agricultural performance negatively in the long run; the effect on economic growth is negative in the short run but positive in the long run. Also, there is no evidence for the short-run impact of labour on both agricultural performance and economic growth; however, the long-run effect is positive and significant. There is a piece of strong evidence supporting financial development as an agricultural performance and economic growth drivers both in the short and long run. Per capita income which reflects the individual purchasing power impacted agricultural performance only in the long run whereas its impact on economic growth which is only significant in the long run is negative. Further, it is revealed in the study that the role played by agricultural performance in driving economic growth cannot be overemphasised both in the short run and long run. Therefore, this study recommends strengthening institutional capacity for financial sector monitoring through legislation and countercyclical buffers, promoting R&D and policies to enhance industrial and agricultural performance, and advancing financial institutions to optimise agriculture-linked industrialisation. Also, governments should encourage affordable agricultural credit, supported by awareness campaigns, and prioritise apprenticeship programs, reskilling and technology-driven R&D to boost labour productivity and sustain economic growth in Sub-Saharan Africa.
Banking instability, as a result of the sovereign risk emergence, triggered, over time, the need for a detailed diagnosis by decision-makers from the monetary authorities, being a theme of permanent relevance and complexity among economic policies. The main issue is related to the existence of a very close, dependent link between the probability of banking instability and sovereign risk. The rigor of the issue requires an in-depth analysis; therefore, this paper aims to capture aspects of micro and macroprudential, based on a panel data set for European Union countries, starting from 2005. In this context, the research identifies liquidity and solvency as the main vulnerabilities to macroeconomic stability, based on its objectives. This is achieved through a micro-level analysis of credit institutions and the use of macroprudential assessment tools, applying multivariate regression and vector autoregressive models, and complemented by unifactorial and multifactorial resilience scenarios to extreme but plausible events. Another objective is to develop a diagnostic framework that enables the assessment of banking performance sensitivity to government bond yield dynamics through both market and credit risk channels. The importance of this research and the estimated results lie in identifying the negative impact of rising government bond yields on banking profitability, particularly on capital adequacy. On the other hand, the originality of this research lies in the estimates that contribute to shaping a set of policy options within the economic policy mix and to formulating proposals for preventive measures aimed at mitigating systemic risks arising from the interaction between sovereign risk and the likelihood of banking instability.
Digital transformation increasingly positions business informatics at the core of organisational competitiveness, driving companies to digitalise and automate their business processes. In this context, low-code/no-code (LCNC) platforms have emerged as a promising solution within business information systems, enabling rapid development of process-oriented applications with minimal or no programming. By empowering employees without formal IT backgrounds to participate in system development, LCNC platforms address the shortage of IT professionals and help bridge the gap between technical and domain-specific business knowledge. Although vendors emphasise advantages such as ease of use, accelerated development cycles, reduced costs, lower IT dependency and enhanced process innovation, they often overlook the organisational, technological and governance challenges associated with LCNC adoption. This paper systematically identifies key inhibitors and LCNC implementation through a comprehensive literature review, followed by an assessment of their significance across multiple case studies with LCNC users. Seven major inhibitors are identified: vendor lock-in, security and compliance risks, integration challenges, limited scalability, insufficient documentation, limited testing support, and lack of flexibility. Case study findings indicate that lack of flexibility and customisation, vendor lock-in and insufficient testing support represent the most critical barriers. The paper presents preliminary insights from an ongoing investigation into LCNC development.
This research investigates how service companies can optimize digital marketing strategies to harness emerging opportunities and overcome persistent challenges in a rapidly evolving digital economy. Despite the sector’s growth and the availability of established frameworks–such as Rogers’ diffusion of innovation, the AIDA model, and the 7Ps marketing mix–a gap remains in understanding how these models can be practically integrated to address barriers like resistance to change, digital skills shortages, and technology integration. The study adopts a qualitative, three-stage methodology: (1) analysis of trends including artificial intelligence, immersive technologies, hyper-personalization, and ethical practices; (2) benchmarking best practices through industry reports and agency case studies, focusing on personalized customer experiences and omnichannel engagement; and (3) in-depth examination of documented digital transformation cases from leading firms such as Nike, IKEA, Lego, and Starbucks. The findings demonstrate that digital marketing enhances visibility, targeting, engagement, and operational efficiency while also presenting challenges related to change management, skills gaps, integration complexity, and data security. The study contributes an integrated framework linking opportunities, challenges, and success strategies and recommends further research on small and medium-sized enterprises and the ethical dimensions of digital marketing. This work offers practical advice for service companies navigating digital transformation.
This article explores the factors driving labour market variability using principal component analysis (PCA) on data from 191 countries. With a focus on economic, demographic, and institutional variables, it aims to identify the primary components influencing labour market dynamics on a global level. Key variables include GDP per capita, Human Development Index (HDI), unemployment rates, poverty rates, indices on the labour freedom index and perception of corruption, average wages, and demographic indicators such as population structure and migration rates. Following data collection, the study employed multiple imputation to handle missing values, ensuring a robust dataset suitable for PCA. The PCA results reveal that the first principal component, comprising indicators of economic prosperity and human development, such as GDP per capita and HDI, explains the largest share of variability in the labour market data. Subsequent components, though contributing less individually, highlight structural factors, including average working hours, urbanization, and demographic influences like migration and age distribution. Together, these components suggest that high standards of living and economic stability play a critical role in shaping the labour market, while secondary factors like urban demographics and migration trends also impact labour dynamics. These findings support the hypothesis that economic and human development indicators significantly drive labour market variability. Implications for policymakers include focusing on economic stability and enhanced social outcomes to foster workforce engagement. The study underscores the importance of tailoring policies to account for demographic factors and calls for further research incorporating additional socioeconomic variables to deepen understanding of labour market dynamics.
This study delves into the intricate relationship between information and communication technology (ICT) and poverty in the context of South Africa, exploring the mutual feedback shocks that dynamically shape both domains using data from World Development Indicators for the period 1990-2021. Employing a comprehensive analytical framework, the study investigates how advancements in ICT, an index of computers, mobile phones and internet, influence poverty rates, proxied by head count ratio, in the short run and long run and, conversely, how the socio-economic conditions associated with poverty feedback into the ICT landscape. Correlation test, granger causality test, co-integration test and VAR/VECM models were utilised in an endeavor to seek answers to the questions. The empirical results showed that there is a relationship, with ICT truly causing poverty in South Africa. The VAR/VECM established that there exists a long run relationship between ICT and poverty in South Africa, at 10% significance level, and the variance decomposition further confirmed some significant short run feedback shocks between ICT and poverty. It is highly recommended that the South African government put in place sound and friendly ICT policies, more especially to the marginalised and poor townships where a lot of SMMEs are trying to thrive. Skills development and an increase in public expenditure on ICT is recommended, as an effort to eradicate poverty through ICT. Through empirical analysis, the complex dynamics that underscore this mutual feedback loop were exposed, shedding light on the potential mechanisms for breaking the cycle of poverty through strategic ICT interventions. This research not only contributes to the academic discourse on technology and development but also provides practical insights for policymakers and stakeholders seeking sustainable strategies to address poverty challenges in South Africa.
Natural gas is a key source of energy and an important industrial input in electricity generation. The three gas directives from the beginning of the 21st century liberalised the European gas market. They incentivised a switch from Oil Price Indexing to a Gas-on-Gas price-setting mechanism, which made the deregulated market an interesting object of research. The drivers of natural gas prices in the European market are examinee. A VAR model with exogenous variable (VARX) is used to estimate the effects of chosen factors. The impulse-response function shows that in the short run, the European gas market is sensitive to imports of liquid natural gas and gas storage, whereas in the long run, it is highly dependent on coal, with air temperature and oil prices playing a negligible role. Forecast error variance decomposition results indicate the relationship between natural gas and coal prices in Europe. Cumulatively, approximately 64% of natural gas price variation is explained by variations in coal prices, gas storage and liquid natural gas imports, with coal prices being the single most important driver of natural gas prices, contributing to 35% of price variation.
This study aims to analyse the relationship between the effectiveness of financial institutions and foreign investment flows in light of time-series data. The results showed that the financial institutions effectiveness index remained relatively stable during the studied period, while the foreign investment flow index experienced noticeable fluctuations. Using unit root and cointegration tests, it was found that the two time series, FIEI and DFI, are stationary at level (I(0)) and do not suffer from unit root problems. Furthermore, the results of the bounds test showed the existence of cointegration between the two indices at 1%, 5% and 10% significance levels. Through the standard model, it was found that the FIEI (-1) index has a positive and statistically significant impact on foreign investment flow, while the FDI index did not show a significant effect. The equilibrium correction rate (ECT) was found to be 41.79%, indicating a continuous correction of the gap between actual and balanced values.
This study investigates the effects of South Africa’s macroeconomic factors on youth entrepreneurship using the Auto-Regressive Distributed Lag (ARDL) model, with quarterly data spanning from 2008Q1 to 2022Q4. The analysis reveals that macroeconomic variables, including GDP, human capital, interest rates, gross fixed capital formation, and youth unemployment influence youth entrepreneurship in both short and long runs. Notably, human capital and interest rates show significant relationships with education fostering entrepreneurship, while high interest rates constrain it. Although GDP and unemployment have positive associations with entrepreneurship, their effects are not statistically significant. The findings highlight the need for policies that prioritise youth entrepreneurship through improved education, supportive infrastructure, and alternative financing mechanisms. Such interventions could enhance youth-led entrepreneurial activities, mitigate unemployment, and promote sustainable economic growth. The study underscores the importance of targeted macroeconomic strategies to empower South African youth entrepreneurs and addresses gaps in existing literature on the economic determinants of entrepreneurship.
