Search Results (4,704)

This study investigates the impact of revenue diversification on the performance and stability of listed Tunisian banks over the period 2008–2023, with the objective of assessing whether diversification strategies enhance bank performance and promote financial stability in an emerging-market context. The analysis relies on a panel dataset of Tunisian listed banks and employs a two-stage least squares (2SLS) estimation approach to address potential endogeneity issues, using ownership structure as an instrumental variable. Bank performance is measured by Return on Assets (ROA) and Net Interest Margin (NIM), while financial stability is captured by the Z-score. The empirical results show that revenue diversification has a positive and significant effect on bank performance, as measured by ROA, and on financial stability. However, it exerts a negative and significant impact on NIM, indicating that although diversification improves overall performance and strengthens stability, it may weaken traditional intermediation income. This study contributes to the limited literature on banking in emerging markets by jointly examining performance and stability effects while addressing endogeneity concerns through robust econometric techniques, and by providing new evidence from the Tunisian banking sector, which has experienced significant political and economic disruptions during the study period. Full article

This article presents a systematic survey of six prominent quantum computing applications in finance, unified under the paradigm of optimization as the foundational use case from which derivative applications are constructed. We formalize the transition from the classical Markowitz portfolio optimization framework to a quantum implementation via the Quantum Approximate Optimization Algorithm (QAOA), including explicit mathematical derivations, theoretical performance bounds, and convergence guarantees. Beyond algorithmic formalism, we critically assess prevailing hardware limitations, focusing on noise thresholds and coherence constraints that currently preclude a demonstrable quantum advantage over classical counterparts. Furthermore, we address the underexplored institutional prerequisites for financial deployment, including regulatory compliance, model validation protocols, and structural barriers to adoption. We conclude that despite ongoing hardware maturation, proactive engagement with quantum algorithm development is imperative for financial institutions to preempt technological obsolescence upon the achievement of hardware parity. Full article

Stationary energy storage (SES) is increasingly needed to integrate variable renewable generation and improve consumer self-consumption, but technology choices involve associated trade-offs between cost, efficiency, and life-cycle impacts. This study evaluates the role of second-life lithium-ion (Li-ion) batteries repurposed from electric vehicles for stationary applications, compared to lead-acid (Pb-acid) batteries and power-to-hydrogen-to-power (PtH₂P) systems. We develop an optimization-based sizing and dispatch framework using measured PV–load profiles and hourly market electricity prices, and evaluate performance per 1 MWh delivered to the load over a 10-year life cycle. Economic performance is quantified through discounted cash flows equal to levelized cost of storage (LCOS), while environmental performance is assessed through life-cycle metrics with explicit representation of recycling and second-life credits. In addition to global warming potential (GWP), the analysis considers additional resource and impact metrics, as well as key operational efficiency metrics, including bidirectional consumption efficiency, autonomy, and share of self-consumption/export of photovoltaic systems. Scenario and sensitivity analyses examine the impact of policy and financial parameters, in particular feed-in tariff remuneration and discount rate, on the comparative ranking of technologies. The results highlight how circular economy pathways, especially second-life distribution for Li-ion batteries and high end-of-life recovery for lead-acid batteries, have a significant impact on the life-cycle burden for delivered energy, while market-driven conditions for dispatching and export activities shape economic outcomes. Overall, the proposed workflow provides a transparent, circularity-aware basis for selecting stationary storage technologies associated with photovoltaic systems, under realistic operational constraints. Full article

This study investigates the relationship between banking sector stability and economic growth in Ethiopia, employing a dynamic panel data approach with the Two-Step System Generalized Method of Moments (GMM). The analysis uses a balanced dataset from 13 Ethiopian commercial banks covering 2014 to 2023, gathered from the World Bank database, the National Bank of Ethiopia, and audited financial statements. Banking sector stability is assessed using indicators such as Z-score, non-performing loan (NPL) ratio, capital adequacy ratio (CAR), liquidity ratio (LR), return on assets (ROA), and loan-to-deposit ratio (LDR), along with key macroeconomic and institutional factors. The results show that banking stability, as indicated by Z-score, liquidity ratios, and profitability, has a positive and significant effect on economic growth, confirming the sector’s role in promoting development. Surprisingly, a positive correlation between NPLs and economic growth suggests unique structural features in the Ethiopian banking system that warrant further investigation. Other variables, such as inflation rates, government expenditure, and gross domestic savings, positively influence economic growth, whereas foreign direct investment is negatively associated with it. The study highlights the importance of enhancing the stability of the banking sector by implementing robust regulatory frameworks, prudent risk management practices, and improved profitability to support sustainable economic development in Ethiopia, while calling for additional research into the unexpected effects of NPLs and FDI amid ongoing financial reforms. Full article

Financial distress prediction remains a central topic in corporate finance and risk management, with extensive research devoted to improving classification accuracy through increasingly sophisticated statistical and machine learning techniques. Nevertheless, the influence of data preparation on predictive performance has received comparatively less systematic attention. This study examines how an economically grounded data-preparation process affects the predictive performance of selected statistical and machine-learning models dedicated to predicting corporate financial distress. Using the chosen financial ratios, generally accepted indicators of corporate financial stability and economic performance, financial distress models are estimated on both raw, unprocessed input data and pre-processed data involving the exclusion of economically implausible accounting values, treatment of missing observations, and class balancing. In light of the above, the study adopts a structured methodological approach to assess the predictive performance of selected classification models, namely decision tree algorithms (CART, CHAID, and C5.0), artificial neural networks (ANNs), logistic regression (LR), and linear discriminant analysis (DA), using confusion-matrix–based evaluation and a comprehensive set of evaluation measures. The results suggest that the process of input data preparation is a critical factor, significantly improving the predictive performance of financial distress prediction models across most modelling techniques employed. The most pronounced gains are observed in decision tree models. ANNs also demonstrate marked improvement after input data preparation, whereas LR benefits more moderately, and linear DA remains limited despite preprocessing. The average gain in accuracy across all six modelling techniques, calculated as the difference between pre-processed and raw performance for each method and averaged across methods, was approximately 15.6 percentage points, with specificity improving by approximately 26.9 percentage points on average, amounting to roughly half the performance variation attributable to algorithm choice, which underscores that data preparation is a primary determinant of model reliability alongside algorithm selection. A step-level detailed analysis further shows that missing value imputation is the dominant driver of improvement for tree-based models, while class balancing contributes most for ANNs and logistic regression. The findings highlight that reliable financial distress prediction depends not only on technique selection but also on the consistency and economic plausibility of the input data, underscoring the central role of structured data preparation in developing robust early-warning models. Full article

The concentration of tourist flows along short urban links caused by cruise stops in medium-sized Mediterranean ports exacerbates traffic and localized environmental externalities. This study evaluates the willingness to pay (WTP) of cruise passengers for an electric tram that would connect the Gaženica Port with Zadar’s historic center, an intervention designed to cut travel time and reduce on-street congestion and emissions. Over the course of two seasons, a two-wave, two-site, in-person survey was conducted at the port and in the city center. The instrument adopts a double-bounded dichotomous choice (DBDC) contingent valuation design with randomized starting bids that were calibrated using a pre-test that benchmarked prevailing transport pricing. Primary WTP estimates are obtained from a binary choice model with socio-demographic and environmental covariates; whereby inference relies on cluster-robust errors. Robustness is assessed through three complementary checks that do not require additional data: (i) a bivariate specification to account for within-respondent correlation between first and follow-up bids; (ii) Turnbull nonparametric bounds for the interval-censored WTP distribution; and (iii) starting-point tests using split-sample estimation and bid-set indicators. A spike adjustment based on “no–no at the lowest bid” responses is explored where appropriate. Beyond its methodological contribution, this research advances the sustainable tourism development discourse by quantifying visitors’ financial support for low-emission urban mobility infrastructure that mitigates environmental stresses while preserving residential life quality. The results integrate cruise tourist management with the more general goals of resilient and sustainable urban destinations by offering a decision-ready value input for port-city mobility planning in historic Mediterranean centers. Full article

This paper examines how scam compounds, money mules and crypto-assets operate as interdependent elements of contemporary money-laundering chains. It assesses whether existing anti-money laundering (AML) and crypto-asset regulatory frameworks are capable of disrupting these chains holistically, rather than addressing individual components in isolation, with particular reference to scam-compound activity in Southeast Asia. The study adopts a qualitative comparative case-study methodology grounded in legal and regulatory analysis. Four empirically grounded cases are examined: two Southeast Asian scam-compound enforcement cases (Cambodia and Myanmar) and two European crypto-asset seizure cases (Ireland and Italy). Judicial decisions, enforcement actions and regulatory instruments are analysed through a chain-based analytical framework aligned with Financial Action Task Force (FATF) standards, the EU Markets in Crypto-Assets Regulation (MiCA) and the Anti-Money Laundering Authority (AMLA) framework. The analysis reveals a structural divergence in enforcement strategies: Southeast Asian responses increasingly prioritise network- and infrastructure-level disruption of scam compounds, whereas European approaches remain largely centred on post-offence crypto-asset seizure through traditional proceeds-of-crime mechanisms. Across all jurisdictions, money mules emerge as a critical yet systematically under-regulated intermediary layer enabling the resilience of crypto-laundering operations. The paper advances existing AML typologies by conceptualising scam compounds, money mules and crypto-assets as interconnected components of a single crypto-laundering chain. This chain-based perspective offers a novel analytical and regulatory lens for understanding organised crypto-enabled fraud. The study is based on a qualitative, case-based design and does not aim for statistical generalisation. However, the analytical framework developed is transferable to other jurisdictions experiencing similar scam-compound and crypto-laundering dynamics. The findings suggest that effective AML enforcement requires coordinated intervention across multiple nodes of the laundering chain, including scam compound infrastructure and money mule networks, alongside traditional asset-seizure mechanisms and CASP supervision. By highlighting the structural links between scam compounds, coercive labour and crypto-laundering mechanisms, the paper underscores the broader social harms of crypto-enabled fraud and the need for integrated regulatory responses that address both financial crime and human exploitation. Full article

Credit risk prediction is central to financial stability and regulatory compliance, guiding lending decisions and portfolio risk management. While traditional approaches such as logistic regression and tree-based models have long been the industry standard, recent advances in deep learning (DL) have introduced architectures capable of capturing complex nonlinearities, temporal dynamics, and relational dependencies in borrower data. This study provides a comprehensive review of DL methods applied to credit risk prediction, covering multi-layer perceptron, recurrent and convolutional neural networks, transformer, and graph neural networks. We examine benchmark and large-scale datasets, highlight peer-reviewed applications across corporate, consumer, and peer-to-peer lending, and evaluate the benefits of DL relative to classical machine learning. In addition, we critically assess key challenges and identify emerging opportunities. By synthesising methods, applications, and open challenges, this paper offers a roadmap for advancing trustworthy deep learning in credit risk modelling and bridging the gap between academic research and industry deployment. Full article

Efficiency refers to the performance level corresponding to using minimal inputs to achieve the maximum possible outputs. Despite its importance to the Canadian economy, such performance assessments has rarely been undertaken in the hardware retail industry in recent years. We present the results of a recent study of the relative efficiencies for three major chains of hardware and renovation retail stores operating in Canada (Home Depot, Lowe’s and Rona). We use the classic and bootstrap data envelopment analysis (DEA) models to measure performance levels over the 22 years from 2000 to 2021. Overall, the firms exhibited high efficiency during this period, and operations management was the primary source of inefficiency. However, an analysis of trends over the 22 years shows that all three companies experienced periods of declining efficiency at the beginning of the study period, followed by a phase of recovery that appears to have accelerated towards the end of the study period. Our longitudinal analysis also indicates that recent shocks and crises have impacted the firms. The succession of crises at the end of the 2000s, the 2007 forestry crisis in Canada, and the 2008 global financial crisis led to the lowest period of efficiency for all the firms, from which they started rebounding in 2011. The specific impact on Rona can explain Lowe’s acquisition of Rona in 2015. However, such a move did not seem to have had a significant improvement beyond accelerating a recovery that had started a few years earlier. This may explain Lowe’s sale of all its Canadian operations in 2022, leading to a new firm called Rona+. Finally, the COVID-19 pandemic also seems to have had a similar effect: accelerating the recovery from the 2008 financial crisis that the firms had started in 2011. Full article

Background: The present study aimed to assess the caregiving burden among family caregivers of adult patients with various cancer types and stages in the Kingdom of Saudi Arabia (KSA), and to examine the associations with caregiver and patient characteristics. Materials and Methods: A cross-sectional study involving 212 family caregivers of cancer patients was conducted between March and April 2024 at King Abdullah Medical City in Makkah, KSA. The Arabic version of the Zarit Burden Interview (ZBI) scale was used to assess overall and specific burdens. Associations between overall burden and sociodemographic variables were analyzed using significance tests. Results: Over half (55%) of participants experienced burden, with a mean ZBI score of 26.33 ± 16.86, indicating a mild to moderate level. Low levels of psychological (7.34 ± 5.41), social (2.27 ± 2.93), physical (1.96 ± 2.22), and financial (1.22 ± 1.41) burdens were found. Financial difficulties and patient immobility significantly contributed to higher burden scores. Caregivers with financial hardships scored higher (31 ± 14.8 vs. 24 ± 17.3, p = 0.01), and those caring for bedridden patients experienced greater burdens (38 ± 21.8 vs. 18 ± 12.5, p = 0.001). Conclusions: Although financial difficulties and patient immobility significantly contribute to caregiver burden, the overall burden in the Makkah region remains relatively moderate. Strong cultural and familial support systems in KSA may alleviate challenges, yet coping strategies targeting financial and physical burdens are necessary. Full article

Dementia caregiving represents a major public health challenge, with spousal caregivers assuming the greatest burden. Spouses, themselves typically older adults, provide high intensity, long-term, and largely unpaid care across all stages of cognitive decline. Despite their central role in dementia care, the health consequences experienced by spousal caregivers remain insufficiently characterized in the literature and inadequately addressed in clinical and public health practice. This structured narrative review synthesizes current evidence on the multidimensional impact of dementia caregiving on the physical, psychological, cognitive, social, and financial health of spousal caregivers. It further contextualizes these consequences within the trajectory of dementia progression, and identifies interventions, support systems, and policy considerations necessary to mitigate caregiver burden. Spousal caregivers experience disproportionate burden due to continuous, escalating responsibilities that often mirror the progressive deterioration of their partners. Emotional burdens, including uncertainty during pre-diagnostic stages, role strain, conflict, loss of intimacy, and anticipatory grief. Physically, spouses endure musculoskeletal strain, sleep disruption, poor nutrition, and heightened frailty risk. Psychologically, spousal caregivers exhibit elevated rates of depression, anxiety, loneliness, and stress-related disorders. Socially, caregivers experience substantial isolation, stigma, and erosion of social networks. Financial hardship, including early retirement, reduced employment, and uncompensated care hours, further exacerbate stress. Evidence suggests that chronic caregiving stress contributes to biological changes such as immune dysregulation, inflammation, acceleration, aging, and potential cognitive decline in caregivers themselves. Caregiver burden influences patient outcomes as evidenced by increased emergency department use, falls, and earlier institutionalization in persons with dementia whose caregiver is subjected to a high burden. Current care models rarely include routine, caregiver assessment or structured guidance following diagnosis, resulting in substantial unmet needs. Effective mitigation requires integrated, stage-sensitive interventions, including psychosocial support, caregiver education, respite services, culturally tailored programs, and digital health tools, alongside broader policy reforms to reduce financial and structural barriers. Full article

Building Information Modelling (BIM) has emerged as a transformative approach for improving efficiency, coordination, and sustainability in the construction industry; however, its adoption in developing regions remains limited. In the Kurdistan Region of Iraq (KRG), BIM implementation—particularly within the residential construction sector—remains at an early stage and lacks comprehensive empirical investigation. This study aims to assess stakeholders’ perspectives on current BIM practices, identify key adoption barriers, and develop a context-specific strategic framework to support BIM implementation. A mixed-method research design was employed, incorporating literature review, expert validation through semi-structured interviews, and a structured questionnaire survey. A total of 319 valid responses were analyzed using descriptive statistics, Relative Importance Index (RII), Cronbach’s alpha for reliability, Spearman’s rank correlation, independent samples t-tests, and one-way ANOVA. In addition to ranking barriers, an inter-barrier correlation analysis was conducted to examine the relationships, clustering patterns, and hierarchical structure of BIM adoption challenges. The results indicate that while BIM awareness is moderately established among stakeholders, its practical application remains limited, particularly beyond the design phase. The most critical barriers include lack of training and expertise, absence of regulatory frameworks and standards, insufficient government support, and financial constraints. The correlation analysis reveals that these barriers are interdependent, with policy and institutional deficiencies acting as root drivers influencing technical, financial, and awareness-related challenges. Based on these findings, the study proposes a four pillar conceptual strategic framework encompassing human capital development, regulatory and standardization enablement, awareness and demand generation, and organizational and collaborative enhancement. The framework is explicitly derived from empirical results, linking barrier clusters to prioritized strategies, thereby enhancing its practical applicability. This study contributes to the existing literature by providing one of the first multi-province empirical assessments of BIM adoption in the KRG residential sector, integrating statistical validation with strategic development, and offering transferable insights for other developing regions at a similar stage of BIM adoption. Full article

This study develops and validates the 5P–ESG Composite Index as a finance-oriented framework for assessing firm-level sustainable-financial performance in emerging markets. It addresses a persistent limitation in ESG measurement, namely the lack of conceptually integrated and decision-useful metrics capable of incorporating not only environmental, social, and governance dimensions, but also institutional and relational dimensions that are especially relevant in heterogeneous emerging-market settings. Conceptually, the proposed framework is grounded in the 2030 Agenda’s 5Ps (People, Planet, Prosperity, Peace, and Partnerships) and extends conventional ESG approaches by explicitly incorporating Peace and Partnerships into firm-level assessment. Methodologically, the index is constructed through sequential indicator selection, data cleansing, winsorization, normalization, pillar-level scoring, and PCA-based endogenous weighting, while its statistical robustness is assessed through internal consistency tests and factorability diagnostics. Empirically, the framework is applied to issuers in the MSCI COLCAP universe, where it proves operationally feasible and suitable for classifying firms into relative performance groups. In addition, a benchmark comparison against a conventional ESG-3 scheme shows that the broader 5P architecture can modify issuer rankings and tercile classification. Overall, the findings support the proposed index as a transparent, auditable, and context-sensitive tool for investors and decision-makers seeking more comprehensive sustainability metrics in emerging markets. Full article

Against the backdrop of China’s “dual carbon” goals, investigating whether market-based environmental regulations can effectively induce technological upgrading is critical for achieving a sustainable low-carbon transition. This study adopts a staggered difference-in-differences (DID) approach within a two-way fixed-effects framework, supplemented by propensity score matching (PSM-DID), to identify the causal impact of the carbon emission trading (CET) pilot policy. The research utilizes a comprehensive panel dataset of A-share listed companies in heavy-polluting industries from 2010 to 2024, incorporating IPC-matched green patent application data to provide a granular assessment of corporate innovation performance. The empirical findings reveal a structural divergence: while the CET policy promotes green innovation in state-owned enterprises (SOEs), it exhibits a potential “crowding-out” effect on private enterprises, a relationship further explained by the mechanisms of carbon information disclosure and financing constraints. These results suggest that the “Porter Effect” in emerging markets is highly conditional on institutional resource endowments, implying that policymakers must complement market incentives with differentiated financial support and enhanced transparency standards to foster a more equitable innovation ecosystem. Full article

The rally to mitigate growing carbon emissions and climate change necessitates decarbonization strategies, with hydrogen emerging as a key candidate option across multiple sectors. This review examines the current state of the hydrogen economy, including production, implementation, and associated risks. Hydrogen’s versatility in industry, transportation, and energy storage is highlighted, alongside the challenges of transitioning from fossil fuel-based production. It explores the current state of hydrogen technologies, differentiating between green, blue, and gray hydrogen production methods, and highlights advancements in production techniques like thermochemical water splitting. Key findings show that while green hydrogen offers the cleanest pathway, high production costs and infrastructure limitations remain significant barriers to widespread adoption. This study also addresses safety concerns and public perception, emphasizing the need for robust risk assessment methodologies and management approaches. Furthermore, this paper underscores the importance of technological innovations, such as high-temperature electrolysis and synergies with renewable energy sources, to enhance efficiency and sustainability. Policy recommendations include financial incentives, regulatory frameworks, and international cooperation to accelerate hydrogen adoption and balance its development with other low-carbon solutions. Full article