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Volume XX, Fall, Issue 3(89), 2025
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This paper solves for equilibria of bargaining games with a seller and a buyer where there is no discounting between periods but players pay fixed bargaining costs for each period they bargain. In this setting, for the seller to cut prices gradually and effectively, the buyer needs to be risk averse. If players are not allowed to terminate bargaining in a finite game, the seller will raise the equilibrium prices. Allowing players to terminate bargaining causes the players to never make a deal with each other. Allowing the buyer to discover the value of the good along with bargaining termination enables the buyer to stop the seller from offering a high price and the seller to engage in price skimming by gradually lowering the price in equilibrium.
Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s history:
Received 25th of June, 2025; Received 29th of July, 2025; Accepted 12th of August, 2025; Available online: 30th of September, 2025. Published as article in the Volume XX, Fall, Issue 3(89), 2025.
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Service performance is the important part of the lodging industry, which relies significantly on the transformation of business strategies. The objective of this study is to examine the relationship between entrepreneurial orientation, digital competence and adaptability, market orientation, and service performance within the lodging case. The study was conducted in the National Capital City (IKN), involving a sample of 153 small and medium-sized enterprise (SME) owners in the lodging industry.
Data collection was carried out through a direct door-to-door survey, and the results were analysed using Ordinary Least Squares (OLS) regression. The authors found that entrepreneurial orientation positively influences digital competence and adaptability, as well as service performance. However, digital competence and adaptability have a negative impact on service performance. Entrepreneurial orientation can positively moderate the relationship between digital competence and adaptability, as well as service performance. Other empirical findings indicate that digital competence and adaptability, when moderated by market orientation, negatively affect service performance. The research design linking the impact of market orientation to lodging service performance has not been extensively explored. Given that digital competence and adaptability are shown to diminish service performance both directly and indirectly through market orientation, it is essential to emphasize these factors in the highly competitive lodging industry. Management in SMEs should leverage entrepreneurial orientation as a crucial differentiator to enhance success and improve service performance.
Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s history: Received 25th of June, 2025; Revised 29th of July, 2025; Accepted 12th of August, 2025; Available online: 30th of September, 2025. Published as article in the Volume XX, Fall, Issue 3(89), 2025.
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As artificial intelligence technologies become increasingly prevalent across the financial sector, the interpretability of machine learning models has become a critical concern for regulatory authorities and financial institutions. This study employs SHAP (SHapley Additive exPlanations) to systematically compare the predictive performance and interpretability of five mainstream machine learning models in financial classification tasks. Using a real financial dataset containing 24 financial indicators to train logistic regression, five machine learning models - logistic regression, random forest, XGBoost, LightGBM, and support vector machine - are trained on this dataset. SHAP is then applied to analyse the feature importance patterns across models. Empirical results demonstrate that LightGBM achieves the best predictive performance (accuracy 95.90%, Area Under the Curve (AUC) 99.18%), while XGBoost shows advantages in terms of interpretability. SHAP analysis identifies those prior earnings per share is the most critical feature, and the Top-K overlap analysis reveals a high degree of consistency among tree-based models in feature importance recognition. This study provides scientific basis for financial institutions to select appropriate explainable AI models, and holds significant importance for enhancing transparency and trustworthiness in financial AI applications.
Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s history: Received 20th of June, 2025; Revised 18th of July, 2025; Accepted 15th of August, 2025; Available online: 30th of September, 2025. Published as article in the Volume XX, Fall, Issue 3(89), 2025.
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This paper estimates the size and timing of state-level shifts of the Beveridge Curve to examine changes in US labour market conditions during the Great Recession. Compared to a benchmark based on each state’s share of unemployed, states in the Southwest, Southeast, and Mid-Atlantic experienced excess shifts. The timing of the shifts is heterogeneous, with 35 states shifting between November, 2009 and March, 2010. Regression evidence shows that population growth, higher construction and natural resource employment, house price appreciation, and urbanization explain the size of the state shifts, which suggests that diverse factors contributed to the shifts in the Beveridge curve.
Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s history: Received 23rd of June, 2025; Revised 29th of July, 2025; Accepted 12th of August, 2025; Available online: 30th of September, 2025. Published as article in the Volume XX, Fall, Issue 3(89), 2025.
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This study evaluates the effectiveness of advanced quantitative techniques, Monte Carlo simulations, AI-driven models, and Genetic Algorithms in enhancing investment portfolio management beyond Traditional Modern Portfolio Theory limitations. Analysing financial data from 2014-2024, this study assessed performance using Sharpe Ratio, Value-at-Risk, and Conditional Value-at-Risk across various market scenarios including black swan events. Findings demonstrate that Genetic Algorithms achieved the highest risk-adjusted returns while minimizing volatility, AI-driven models provided superior adaptability to market fluctuations, and Monte Carlo simulations significantly improved risk assessment compared to traditional approaches. The integration of green bonds into AI-optimised portfolios successfully balanced financial performance with sustainability objectives, appealing to environmentally conscious investors. This research confirms that AI and Genetic Algorithm approaches consistently outperform traditional models in optimising risk-adjusted returns under volatile conditions. Portfolio managers should consider implementing hybrid quantitative approaches that combine AI-based decision-making with Monte Carlo stress testing to enhance investment resilience and strategic planning in dynamic financial environments.
Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s history: Received 3rd of July, 2025; Revised 29th of July, 2025; Accepted 2nd of September, 2025; Available online: 30th of September, 2025. Published as article in the Volume XX, Fall, Issue 3(89), 2025.
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Considered one of the key drivers of recent economic development and a lever for improving individual and collective well-being, education represents a strategic investment. This study, therefore, investigates the effect of internal and external remittances on the education of children in rural Burkina Faso, using a multinomial endogenous treatment effect model and data from the National Land Management Program covering 6,224 children across 1,827 households. The results show that both internal and external remittances significantly improve primary education by increasing children's likelihood of school enrolment and reducing their risk of dropping out. At the secondary level, external remittances continue to have a positive effect on education, while internal remittances tend to reduce the likelihood of enrolment and increase dropout risk.
Furthermore, the findings reveal that external remittances have the strongest impact on enrolment, while internal remittances are more effective at reducing dropout risk at the primary level. These results highlight the need to increase investment in educational infrastructure, especially at the secondary level, to enhance the effectiveness of remittances. They also emphasize the importance of raising awareness among migrants and recipients about the long-term benefits of investing in children's education.
Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s history: Received 18th of July, 2025; Revised 19th of August, 2025; Accepted 4th of September, 2025; Available online: 30th of September, 2025. Published as article in the Volume XX, Fall, Issue 3(89), 2025.
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On May 30, 2019, the African Continental Free Trade Area (AfCFTA) came into effect, marking a significant milestone for African leaders in establishing the largest trading area since the founding of the World Trade Organization. Despite numerous studies on its impacts across the continent, limited attention has been devoted to Morocco.
This study evaluates Morocco's trade potential with AfCFTA member states. It examines the economic effects of the agreement using two complementary methods: a gravity model based on CEPII data to explore trade potential and a CGE framework based on the PEP 1.1 model calibrated with SAM 2019 to assess the overall impacts on the Moroccan economy. Results suggest Morocco's simulated exports and imports with Africa could increase by 72% and 65%, respectively. Yet, overall macroeconomic gains are modest: exports rise by only 0.80% and imports by 0.93%. GDP growth is projected to remain limited at 0.76% (basic prices) and 0.55% (market prices), with minimal income improvement for households (0.65%), firms (0.77%), and the government (0.05%). These results underscore the fragility of South-South trade and the need to reinforce North-South cooperation to enhance Morocco's economic benefits from AfCFTA.
Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s history: Received 2nd of August, 2025; Received in revised form 29th of August, 2025; Accepted 12th of September, 2025; Available online: 30th of September, 2025. Published as article in the Volume XX, Fall, Issue 3(89), 2025.
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According to the Treaty on European Union, the composition of the European Parliament must be degressively proportional with respect to the population size of the individual Member States of the European Union. The reference point is always the population data from the year preceding the elections for the five-year parliamentary term. During the term, however, population sizes may change, which can lead to a violation of the principle of degressive proportionality. In this paper, the concept of demographic stability of a degressively proportional allocation rule is defined, and based on this definition, a coefficient is constructed whose maximisation leads to the identification of allocations that are stable in the sense defined above. A rule based on this maximisation has been empirically verified using data from the 2024 – 2029 parliamentary term and changes in population size that occurred by 2025. The verification confirmed the high effectiveness of this rule in preserving the property of degressive proportionality.
Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s history: Received 29th of July, 2025; Received in revised form 16th of August, 2025; Accepted 14th of September, 2025; Available online: 30th of September, 2025. Published as article in the Volume XX, Fall, Issue 3(89), 2025.
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This article evaluates the combined effects of carbon taxation and regional trade liberalization in Morocco using a dynamic Computable General Equilibrium (CGE) model. Calibrated to a 2019 Social Accounting Matrix enriched with trade and emissions data, the model simulates progressive carbon pricing alongside tariff reductions under AfCFTA. The study compares alternative fiscal recycling schemes, including income tax cuts, wage cost reductions, and investment support, to assess their macroeconomic, social, and environmental outcomes. Results show that certain recycling designs can simultaneously promote trade competitiveness, ensure fiscal sustainability, and reduce CO₂ emissions. These findings offer insights for policymakers designing integrated green fiscal strategies in developing countries undergoing trade reforms.
Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s history: Received 2nd August, 2025; Received in revised form 29th of August, 2025; Accepted 15th of September, 2025; Available online: 30th of September, 2025. Published as article in the Volume XX, Fall, Issue 3(89), 2025.
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This paper examines the dual impact of digitalization on the banking sector from 2013 to 2024, using factor analysis and applied economic modelling to assess how digital tools, regulatory frameworks, and institutional performance interact in shaping financial crime dynamics. The results reveal that while digitalization improves operational efficiency and monitoring capacity, it also exposes structural weaknesses that facilitate illicit activities. Policy implications emphasize the need for robust regulation, strategic governance, cybersecurity investment, and capacity building to ensure that digitalization serves as a driver of innovation, trust, and financial stability. This study contributes empirical evidence to the applied economics literature on the complex role of digitalization in both enabling and mitigating corruption risks in emerging markets.
Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s history: Received 25th of August, 2025; Revised 9th of September, 2025; Accepted 18th of September, 2025; Available online: 30th of September, 2025. Published as article in the Volume XX, Fall, Issue 3(89), 2025.
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This study examines the predictive role of social media sentiment in forecasting short-term Bitcoin price changes using econometric and machine learning models. Based on Twitter and Reddit data (2020–2025), we construct a daily sentiment index and analyse its lagged effect on returns. OLS regression and advanced models (random forest, XGBoost) show that a one-unit increase in lagged sentiment predicts a statistically significant 0.24–0.25% rise in next-day returns. Controls include momentum, volatility, and trading volume, with Granger causality tests and VAR confirming sentiment’s leading role. While volume is insignificant, sentiment and momentum are strong predictors. Machine learning models outperform linear baselines, highlighting nonlinear interactions in sentiment-driven markets. Results validate sentiment as a meaningful input for forecasting, with applications to trading bots, real-time risk dashboards, and supervisory tools. The study contributes to applied economics by showing how quantified investor emotion can serve as a leading indicator in volatile cryptocurrency markets. Future research should consider multilingual sentiment, intraday horizons, and cross-asset extensions.
Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s history: Received 5th of August, 2025; Revised 9th of September, 2025; Accepted 16th of September, 2025; Available online: 30th of September, 2025. Published as article in the Volume XX, Fall, Issue 3(89), 2025.
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This study addresses the challenge of sustainable urban development in the context of global climate imperatives and accelerating urbanisation. We analyse the economic and implementation nexus between smart governance, green infrastructure, and sustainable financing. The core objective is to conduct a technical and economic analysis of the governance mechanisms, financial instruments, that facilitate the successful implementation of green infrastructure projects in emerging economies.
Utilising a mixed-methods approach, this research combines a quantitative analysis of investment trends and project outcomes with a qualitative assessment of institutional frameworks and case studies drawn from Kazakhstan’s urban development experience. Our findings indicate that effective smart governance is a critical enabler for attracting and sustaining investment in green infrastructure. Key success factors include the establishment of robust financial mechanisms, such as public-private partnerships, green bonds, and performance-based funding, along with strategic planning and regulatory coordination among various stakeholders. The primary contribution of this research is the development of a comprehensive implementation framework that integrates technical, economic, and institutional dimensions.
Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s history: Received 5th of August, 2025; Revised 3rd of September, 2025; Accepted 23rd of September, 2025; Available online: 30th of September, 2025. Published as article in the Volume XX, Fall, Issue 3(89), 2025.
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The study evaluated the effectiveness of public-private partnerships (PPPs) in Ukraine’s social infrastructure, comparing them with fully public projects in terms of cost-efficiency, implementation speed, employment, and user satisfaction. A mixed-methods design was applied, analysing four PPP cases: Biopharma Blood Center, Zhytomyr Hospital, Rinat Akhmetov Emergency Ambulance Initiative, and EdCamp Digital Education Program. Quantitative analysis using SPSS 28.0 included paired t-tests, multivariate and logistic regressions, and Social Return on Investment (SROI). Qualitative insights were drawn from 15 stakeholder interviews coded in NVivo 12.
The PPPs reduced costs per beneficiary by 24% to 27%, shortened implementation time by five to six months, and improved user satisfaction by more than 35 percent. Access to services in underserved areas expanded by 25% to 30 %, while job creation exceeded that of public projects by 40% to 50%. Regression models confirmed statistical significance (p < 0.05), and robustness was validated through non-parametric testing. Qualitative findings identified five key success factors: strategic alignment, strong governance structures, agile implementation, technological innovation, and regulatory coordination. Internal managerial practices such as KPI dashboards, cross-functional teams, and performance-based decision systems significantly contributed to positive PPP outcomes. The study emphasized that these internal mechanisms, often neglected in policy-oriented analyses, played a critical role in transitional and post-conflict contexts. It recommended standardizing SPVs, implementing digital monitoring tools, reforming licensing protocols, and establishing centralized PPP data systems to enhance scalability and institutional success.
Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s history: Received 5th of August, 2025; Revised 9th of September, 2025; Accepted 23rd of September, 2025; Available online: 30th of September, 2025. Published as article in Volume XX, Fall, Issue 3(89), 2025.
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This paper examines how neonomadism, distributed, mobile teams operating in digital environments such as BIM/CDE platforms, digital twins, and cloud systems, acts as a multiplier of innovation and supports green transformation in emerging economies. Focusing on Kazakhstan’s construction sector, multivariate analysis links R&D intensity and a “neonomadism index” to innovation outcomes, including time-to-market, revenue from new products, and patents per unit of R&D. Results show that digital mobility amplifies the efficiency of R&D, shortens project cycles, reduces rework, and accelerates the adoption of green construction practices. Scenario forecasts for 2025–2027 indicate steady growth of the green segment, with outcomes sensitive to policy interventions and digital adoption levels. Policy implications include mandatory BIM/CDE standards, fast-track e-permitting for green projects, incentives for modular construction, and scaling of green finance instruments. The study positions neonomadism not merely as a labour trend but as a strategic economic driver of innovation, efficiency, and sustainable urban development.
Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s history: Received 5th of August, 2025; Revised 9th of September, 2025; Accepted 22nd of September, 2025; Available online: 30th of September, 2025. Published as article in the Volume XX, Fall, Issue 3(89), 2025.
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This study systematically evaluated integrated risk management (IRM) approaches in global supply chains, examining risk categories, mitigation strategies, technological enablers, and framework gaps. Its aim was to provide a strategic understanding of IRM’s role in enhancing resilience and sustainability in volatile environments. Following PRISMA 2020 guidelines, a structured review of 79 peer-reviewed articles (2020–2025) was conducted across Scopus, WOS, JSTOR, and ScienceDirect. A dual-method approach combining thematic synthesis and quantitative descriptive analysis classified findings by risk type, industry focus, mitigation practices, and integration frameworks.
The study found that operational risks were most frequently addressed, with increased attention to cyber and environmental risks. Integrated frameworks emphasizing redundancy, flexibility, real-time analytics, and governance networking are progressively replacing traditional siloed strategies. Technological tools such as AI, IoT, and blockchain have become central to proactive risk prediction and mitigation. In parallel, good governance networking (GGN) has been shown to significantly enhance logistics efficiency and trade responsiveness, reinforcing operational performance and service delivery. This review underscores a multi-level approach, combining technological, organizational, and governance enablers, as essential for resilient and sustainable supply chains in the post-COVID and digitally transformed landscape.
Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s history: Received 10th of August, 2025; Revised 9th of September, 2025; Accepted 20th of September, 2025; Available online: 30th of September, 2025. Published as article in the Volume XX, Fall, Issue 3(89), 2025.
