Business, Economics and Management

Abstract: Cross-country measures of Islamic finance development are weighted toward realised market outcomes and therefore assign near-identical scores to jurisdictions that differ substantially in institutional preparation but have yet to accumulate Shariah-compliant assets. Uzbekistan is an instance of that configuration and, because the sector is new, one in which the underlying structure can be observed directly. This study examines the first eighteen months of Shariah-compliant finance in Uzbekistan using supervisory returns covering the complete population of licensed providers, together with the banking law that entered into force in June 2026. Financing grew almost eightfold year on year, yet the market is extraordinarily concentrated: the Herfindahl-Hirschman index reaches 6,019, an effective supplier count of 1.66, and 94.0 percent of activity is confined to the capital. Concentration was stable between the two observed quarters while the identity of the dominant providers changed entirely, and the Gini coefficient rose from 0.47 to 0.59. Product structure diversified sharply, the effective number of instruments rising from 1.00 to 1.91 within twelve months, with the profit-and-loss sharing instrument concentrated in the household segment. The evidence indicates that legal permission and market capability are separated by a measurable interval, and that institutional readiness advances ahead of market breadth.

Abstract: Purpose: This paper reviews the evolution, growth trends, regional distribution, determinants, structural constraints, and future prospects of the global takaful and re-takaful industry. Methodology: The study employs a structured review of peer-reviewed literature, institutional reports, regulatory instruments, international standard-setting publications, and industry intelligence to synthesize evidence on market development and emerging trends. Findings: The review highlights rapid growth from a relatively small base. Global Islamic finance assets reached USD 5.98 trillion in 2024, while Islamic insurance represented approximately 1.4% of total assets. Takaful contributions grew by 15.4% in 2024, alongside 16.9% growth in assets. Market activity remains concentrated in the GCC and Southeast Asia, supported by compulsory insurance, Islamic banking expansion, and supportive regulatory and Shariah-governance frameworks. Limited re-takaful capacity, regulatory gaps, and business-model heterogeneity remain major constraints. Implications: Digital distribution, micro-takaful, waqf-based and parametric products, regulatory convergence, and re-takaful capacity building offer important avenues for sustainable expansion. Originality/Value: The study integrates market, regulatory, and institutional perspectives to identify structural drivers and future development pathways.

Abstract: Purpose: The rapid diffusion of artificial intelligence (AI)-enabled automation in credit scoring, underwriting, and risk-monitoring systems is reshaping the credit risk function within India's FinTech lending sector. This study examines how credit analysts' perceptions of AI automation capability influence perceived job insecurity, skill obsolescence, reskilling intentions, perceived job displacement, and career adaptability. Design/Methodology/Approach: Drawing on task-based automation theory, the Job Demands-Resources (JD-R) framework, and technology-acceptance literature, an eight-construct research model with associated hypotheses is developed. Data from credit analysts, underwriters, and credit risk managers employed across FinTech non-banking financial companies (NBFCs), FinTech-bank joint ventures, and digital lending platforms in India are analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS. Findings: The measurement model demonstrates satisfactory reliability and convergent validity (Cronbach's alpha and composite reliability above 0.83; average variance extracted above 0.65 for all constructs), and discriminant validity is established via the heterotrait-monotrait (HTMT) ratio. Structural results indicate that perceived AI automation capability significantly predicts both skill obsolescence perception and job insecurity, which jointly predict perceived job displacement; organizational change readiness and reskilling intention act as adaptive mechanisms that partially offset displacement-related outcomes and support career adaptability. Originality/Value: This study contributes one of the first theory-driven, quantitatively validated models of AI-induced role transformation specific to the credit risk function in an emerging-market FinTech context, offering a replicable measurement instrument and actionable guidance for human resource and risk management practice.

Abstract: Machine learning models are increasingly used in credit risk, financial statement fraud detection, financial distress prediction, and related financial classification tasks with substantial consequences. Yet model comparison still often privileges statistical discrimination even though superior predictive performance does not necessarily imply superior financial decisions. This limitation is acute in settings involving rare events, where class imbalance, probability miscalibration, temporal distribution shift, asymmetric error costs, operational capacity constraints, and governance requirements interact. This conceptual and methodological paper develops an integrated framework for decision validity without introducing a new dataset or estimating additional models. The synthesis draws on two complementary empirical streams: evaluating deep learning for financial statement fraud under severe class imbalance and temporally consistent evaluation of credit risk models that accounts for asymmetric costs under distributional shift. These streams integrate established research on precision and recall, probabilistic calibration, concept drift, classification with unequal costs, profitability-based credit scoring, explainable artificial intelligence, and model governance. The proposed framework consists of five sequential gates: validity for rare event detection, probability validity, temporal validity, decision-utility validity, and governance validity. The gates are intentionally not compensatory: strong performance at a later stage should not automatically offset a fundamental failure at an earlier stage. The paper also maps credit default and financial statement fraud, a minimum reporting standard, and a research agenda for dynamic thresholds, temporal calibration, explanation stability, and utility under capacity constraints. The central conclusion is that model superiority in financial machine learning is conditional rather than absolute and should be asserted only relative to an explicit deployment and decision environment.

Abstract: Enterprise AI applications can create value by reducing the labor required to deliver a business service.The people who use an application, the work it performs, and the units on an invoice need not coincide.This paper distinguishes these elements and shows how they relate. It estimates savings from theshare of professional work an application can support, the time it saves after review and rework, andthe proportion of released capacity that translates into lower labor cost. Those savings bound the pricefrom above. What can be charged within that bound is set by the customer’s best alternative ratherthan by the size of the savings, and a corollary states the range of value shares a vendor can feasiblypursue. The billing unit is then a separate choice, governed by what both parties can verify and bythe incentives each unit creates. An illustrative human resources (HR) and payroll case shows thecalculation. The framework explains when per-worker subscriptions are defensible, when transactionor outcome charges are more suitable, and why a value estimate alone cannot establish the profit-maximizing price. It argues that the quantity on which the value depends most, the conversion ofreleased time into lower cost, is chosen by the buyer and cannot be verified by either party, which iswhy applications of this kind are sold by subscription rather than by result. It provides a practicalmethod for analysis and teaching without assuming a universal rate of AI productivity improvement.

Abstract: This study explores how corporate culture influences the relationship between ESG performance and default risk, using data from 4,524 firms across 2002–2023. Findings reveal a positive and significant association between ESG performance—especially the environmental (E) and social (S) dimensions—and reduced default risk, measured through Merton’s distance-to-default. Companies with strong corporate cultures show a stronger connection between ESG performance and lower default risk. The effect is more prominent in mature firms. Two additional moderating factors—pollution intensity and regulatory enforcement—are also examined. In heavily polluting industries, firms with robust cultures exhibit an even stronger ESG-default risk relationship. Moreover, in countries with weaker enforcement, corporate culture plays a key role in mitigating risk both directly and by enhancing ESG’s effectiveness. Robustness checks support all results, emphasizing the importance of integrating ESG strategies with corporate culture and external context to better manage financial risk.

Abstract: This study examines the relationship between eco-innovation (EI) and working capital efficiency (WCE), with corporate social responsibility (CSR) acting as a mediator. Based on the Resource-Based View, Stakeholder, Legitimacy, and Shareholder theories, the study examines how well sustainability-focused tactics improve businesses’ operational success. WCE is measured using the cash conversion cycle (CCC) and its components, including accounts payable period (APP), inventory conversion or holding period (ICP), and accounts receivable period (ACP). Based on the panel-data and strong regression models and firm-level controls, time, and industry effects, the results suggest that eco-innovation positively influences the working capital efficiency through the reduction of cash conversion cycles and the improvement of working processes. This relationship is partly mediated by CSR, which indicates that eco-innovation improves efficiency directly and indirectly via the better stakeholder engagement and responsible practices. The research adds to the literature by identifying a connection between sustainability practices and financial performance and the significance of incorporating eco-innovation and CSR to obtain sustainable operational performance.

Abstract: This article examines volatility, tail risk, temporal dependence, scaling behavior and simulation-based uncertainty in daily futures price series for coffee, Brent oil and gold during 2016-2025. The empirical object is defined as a set of provider-reported historical futures series obtained from Investing.com and transformed into logarithmic prices, logarithmic returns, absolute returns, squared returns, annualized 30-day rolling volatility, standardized returns and base-100 indices. In response to the methodological limitations inherent in secondary market-data aggregation, the study does not treat the downloaded series as exchange-certified individual contract histories, nor does it infer unobserved maturity-specific rollover rules. Instead, it positions the data as a transparent financial-risk input and evaluates the consequences of source status, contract identification and calendar irregularity for empirical interpretation. The results show non-Gaussian returns, heavy tails, heterogeneous volatility and stronger dependence in squared returns than in simple returns. Brent oil records the largest tail exposure, the highest kurtosis and the deepest maximum drawdown, while gold exhibits the most visible calendar irregularity. DFA exponents remain close to 0.5, indicating that strong long-memory claims are not supported without additional robustness tests. MF-DFA curves and Monte Carlo fan charts are therefore interpreted as exploratory risk-diagnostic tools rather than confirmatory evidence of multifractality or calibrated forecasts. The article contributes to risk and financial management by offering a cautious, reproducible and empirically delimited framework for comparing commodity futures relevant to Colombia.

Abstract: This study examines the relationship between eco-innovation (EI) and working capital efficiency (WCE), with board gender diversity (BGD) acting as a moderator. Based on the Resource-Based View, Stakeholder, Legitimacy, and Shareholder theories, the study examines how well sustainability-focused tactics improve businesses’ operational success. WCE is measured using the cash conversion cycle (CCC) and its components, including accounts payable period (APP), inventory conversion or holding period (ICP), and accounts receivable period (ACP). Based on the panel-data and strong regression models and firm-level controls, time, and industry effects, the results suggest that eco-innovation positively influences the working capital efficiency through the reduction of cash conversion cycles and the improvement of working processes. Gender diversity in the board moderates this relationship, meaning that diverse boards enhance governance and strategic alignment. The research adds to the literature by identifying a connection between sustainability practices and financial performance and the significance of incorporating eco-innovation and inclusive governance to obtain sustainable operational performance.

Abstract: Corporate Social Responsibility (CSR) has become increasingly relevant in emerging banking systems, yet its financial implications remain debated. This study investigates the impact of CSR on the financial performance of Moroccan banks over the 2014–2023 period. Using a panel dataset of eleven banking groups, we employ static econometric models (fixed effects, random effects, and pooling) and a dynamic Arellano-Bond GMM model to examine whether CSR certification, measured through institutional recognition by CGEM, MASI.ESG, UNGC, or Vigeo Eiris, affects profitability (ROA, ROE, and ROS), solvency (RSC and RCR), and market valuation (MBV). The findings do not reveal a statis-tically robust relationship between CSR certification and financial performance. Only the risk coverage ratio (RCR) exhibits a partially significant association in some static models. These results suggest that CSR practices remain relatively immature in emerging econo-mies and that certification may operate primarily as a legitimacy mechanism rather than a direct driver of financial value creation. The study contributes to the CSR–financial per-formance debate by providing evidence from the Moroccan banking sector and highlights the need for strategically embedded, measurable, and integrated CSR practices to generate financial benefits.

Abstract: Savings and Credit Cooperatives (COACs) within Ecuador’s Popular and Solidarity Economy (PSE) represent the principal source of financial intermediation for households and microenterprises with limited access to the formal banking system. This article examines the determinants of loan delinquency in Segment 4 and Segment 5 SCCs within Ecuador’s PSE framework, employing an unbalanced panel dataset of 368 institutions—220 in Segment 4 and 148 in Segment 5—and 5,056 quarterly observations spanning the period 2020Q1–2025Q4 (24 quarters). The econometric evidence obtained from a dynamic panel model estimated using the Arellano–Bond difference estimator (AB-2SLS), supplemented by seven robustness specifications, reveals a strong degree of persistence in loan delinquency, indicating that deterioration in portfolio quality is unlikely to be corrected spontaneously over time. The results further show that portfolio concentration in microcredit constitutes the most important structural risk factor, reflecting the greater vulnerability of micro-cooperatives to correlated defaults. Institutional size exhibits an asymmetric protective effect, a pattern consistent with the community relationship-banking hypothesis. Time dummy variables indicate that the decline in delinquency observed during 2020 was driven by regulatory payment-deferral measures rather than by a genuine improvement in members’ repayment capacity, whereas the increase observed after 2022 reflects the gradual exhaustion of those temporary policy interventions.

Abstract: Digitalisation has changed the delivery of microfinance, but the presence of a digital transaction does not necessarily mean that a borrower has acquired meaningful digital financial capability. This study examines that distinction among 663 active microfinance borrowers in Ranchi district, Jharkhand, India. Drawing on the Technology Acceptance Model (TAM) and selected constructs from the Unified Theory of Acceptance and Use of Technology (UTAUT), the study evaluates the roles of digital literacy, perceived ease of use, perceived usefulness, trust in the microfinance institution, trust in technology, perceived risk, social influence, and facilitating conditions in shaping behavioural intention. A borrower-centred distinction is introduced between institutionally required digital transactions and autonomous digital financial use. The empirical pattern is striking: 84.9% of respondents reported digital loan repayment, whereas only 12.1% reported fund-transfer use. This difference is interpreted as a Compliance–Autonomy Gap rather than as evidence of broad-based digital financial adoption. Descriptive results also indicate a marked Trust Asymmetry: trust in the microfinance institution was high (M = 4.37, SD = 0.50), while trust in technology was considerably lower (M = 3.38, SD = 1.16). In the reported OLS model, perceived usefulness (β = .2766, p = .002), social influence (β = .2372, p = .006), and facilitating conditions (β = .3162, p = .006) were positively associated with behavioural intention, while trust in technology (β = −.2752, p = .043) and perceived risk (β = −.2735, p = .043) were negatively associated. Digital literacy, perceived ease of use, and trust in the MFI were not significant at the 5% level. However, severe multicollinearity and heteroscedasticity substantially qualify the coefficient-level interpretation. The study therefore contributes a conceptual framework—the Trust-Enabled Phygital Adoption Framework (TEPAF)—that views digital microfinance adoption as a progression from institutional compliance to assisted use, confidence, independent use, and ultimately financial autonomy. The central implication is that digital inclusion should be assessed not only by transaction volume but also by who performs the transaction, how independently it is performed, and whether the borrower can extend digital finance beyond institutionally required activities.

Abstract: Financial statement fraud detection has progressed from ratio-based screening and statistical classification toward machine learning, ensemble methods, and explainable artificial intelligence. Yet methodological progress has been evaluated predominantly within the datasets and institutional environments in which fraud models are developed. Strong predictive performance in a development sample does not establish that a model will remain reliable when applied to new firms, later periods, different industries, or different institutional environments. This study addresses this problem by developing a conceptual framework for the predictive transportability and external validation of financial statement fraud detection models. Predictive transportability is defined as the degree to which a model developed in a source environment retains acceptable predictive behavior in a specified target environment. The framework proposes four analytical dimensions of transportability: temporal, cross-firm, cross-industry, and cross-institutional. It further integrates dataset-shift concepts with external-validation logic and proposes a lifecycle comprising model development, target-context assessment, shift diagnosis, external validation, recalibration or adaptation, deployment, and continuous monitoring. The study argues that internal predictive performance is evidence of model behavior under observed conditions rather than proof of universal validity. It therefore shifts the central research question from “Does the fraud model work?” to “Where, when, and for whom does the model remain reliable?” The resulting framework provides methodological guidance for evaluating financial statement fraud models beyond their original development samples and establishes testable propositions for future temporal, firm-level, sectoral, and cross-country validation studies.

Abstract: Financial statement fraud research has progressed from rule-based and statistical screening toward machine learning and explainable artificial intelligence (XAI). Recent finance and auditing literature has also begun to frame AI as part of human–AI decision and governance systems rather than as a stand-alone predictive tool. Yet a narrower audit-specific problem remains unresolved: how a model-generated fraud-risk signal should be evaluated, challenged, and translated into a proportionate audit response when explanations may be unstable, error consequences are asymmetric, and audit resources are constrained. This conceptual article develops a Human–AI Decision Governance Framework for financial statement fraud risk assessment. Using theory synthesis and conceptual model development, the framework integrates six layers: fraud-risk signaling, explanation assurance, professional judgment, decision utility, audit response, and governance with feedback. It treats AI outputs as decision inputs rather than fraud conclusions and distinguishes model performance from explanation reliability, professional validity, and decision utility. Six propositions specify testable relationships among predictive performance, explanation quality, professional skepticism, resource-sensitive thresholds, human override, and lifecycle governance. The contribution is deliberately domain-specific: it connects fraud analytics and XAI to the auditor’s assessment and response process, including the risks of material misstatement due to fraud under ISA 240 (Revised), rather than claiming a generic theory of human–AI governance. The framework provides a structured research agenda for behavioral, archival, simulation, and field validation.

Abstract: We begin with a simple but uncomfortable question for many firms: if an environmentally unfriendly company decided to become environmentally performant, what would actually happen to its turnover? Would sustainability represent merely ’the price of doing good’, or would it be closer to an investment with a financial return? Should shareholders view it as a financial risk or as a financial opportunity? To answer these questions, our research combines a measurable Environmental Performance (EP) index, firm-level data from all large Romanian companies reporting sustainability measures, and a combination of deep learning and optimization techniques. We provide evidence that becoming environmentally performant is less about incurring costs for ethical principles and more about creating opportunities for improved financial performance. Environmentally unfriendly firms that would transition toward sustainability would experience a median turnover increase of 3.52%. Moreover, 56.64% of firms would experience a positive effect, with a median gain of 15.61% within this group. For the remaining 43.36%, the median decline of 9.38% is more appropriately interpreted as a short-term adjustment cost rather than a structural penalty, consistent with the existing literature.

Abstract: Autocallable exchange-traded funds offer a useful setting for examining how the ETF wrapper changes the delivery of structured, state-contingent payoffs. These funds place barrier-based and path-dependent contracts inside a continuously traded vehicle with daily valuation, secondary-market access, and ETF-style disclosure. The paper studies the complete U.S. autocallable ETF market using a hand-collected dataset drawn from prospectuses, issuer materials, holdings, collateral information, fund size, premiums and discounts, bid-ask spreads, distribution policies, and current barrier conditions. The evidence shows that the funds are built around different contractual and operating structures rather than a common design. They differ in reference exposure, contract concentration, renewal, collateralization, income delivery, barrier placement, trading costs, and market adoption. The paper contributes to the literature by distinguishing the continuing ETF wrapper from the finite-lived contracts that are called, mature, and replaced within it, and by separating fixed contractual design from changing portfolio state. These differences show that a common ETF form does not imply a common economic exposure. Autocallable ETFs are therefore better understood as platforms for delivering renewable contingent-payoff structures than as a homogeneous extension of conventional income ETFs.

Abstract: Armed conflict threatens financial resilience by weakening the infrastructure, liquidity, information, and repayment conditions on which financial intermediation depends. This study examines whether state-based and non-state conflict affect financial access and use differently across 33 Sub-Saharan African countries. We merge Global Findex 2025 data for 32,951 adults with Uppsala Conflict Data Program fatalities and GSMA mobile money regulatory indicators. Multilevel mixed-effects logit models show that a one-standard-deviation increase in state-based conflict intensity is associated with 3.2 and 1.3 percentage point reductions in formal financial-institution and any-account ownership. State-based conflict is also associated with lower borrowing, domestic remittances, utility payments, and transfer receipt. Non-state conflict produces smaller reductions in formal account ownership, borrowing, remittances, and utility payments. Predicted margins show nonlinear vulnerability, especially for remittances as state-based conflict intensifies. Saving, pensions, and wages are comparatively resilient. The findings connect household financial resilience to banking stability and credit risk by showing how conflict simultaneously weakens transaction continuity and the conditions supporting credit supply. Policy should prioritize payment continuity, agent liquidity, credit-risk management, and resilient financial infrastructure in conflict-exposed emerging markets.

Abstract: ESG investment is widely regarded as a means of promoting sustainable corporate development. However, a high aggregate ESG score may conceal an uneven allocation of efforts across the environmental, social, and governance dimensions. Such an imbalance may create a misleading impression of strong ESG performance and undermine corporate sustainability. This paper examines whether ESG investment imbalance, a covert form of ESG greenwashing, is associated with financial fraud among Chinese listed firms. Using data on China’s A-share non-financial firms from 2009 to 2024, we estimate Probit and Poisson models. The results show that ESG investment imbalance affects financial fraud through five potential mediating channels: financial pressure, financing constraints, internal control quality, information transparency, and earnings management. Heterogeneity tests indicate that the positive effect of ESG investment imbalance on financial fraud is more pronounced among non-state-owned listed firms, firms in competitive industries, and firms operating under high economic policy uncertainty. Moreover, financial fraud induced by ESG investment imbalance reduces both short-term financial performance and long-term market value. Further tests show that external monitoring from institutional investors and financial media significantly mitigates the positive effect of ESG investment imbalance on financial fraud. Therefore, this paper theoretically explores the negative impacts of ESG investment imbalance for the first time, and enriches the literature on ESG greenwashing. At the same time, it offers implications for regulators, investors, and managers seeking to curb ESG greenwashing, improve sustainability governance, and promote more transparent and sustainable corporate behavior.

Abstract: This study critically examines the regulatory and Shariah governance framework of interest-free finance in Ethiopia, focusing on its institutional evolution, regulatory development, governance mechanisms, and persistent challenges. The study employs a qualitative critical review of banking proclamations, regulatory directives, academic literature, industry reports, and relevant international Shariah governance standards. The review finds that Ethiopia has made substantial progress, evolving from interest-free banking windows introduced in 2011 to fully fledged interest-free banks and a more comprehensive legal framework. However, regulatory and Shariah governance arrangements remain fragmented. Key challenges include the absence of a nationally coordinated Shariah governance framework, limited standardization of Shariah oversight and auditing, inadequate professional capacity, unfavorable tax treatment, limited Shariah-compliant liquidity-management instruments, and underdeveloped Islamic capital-market infrastructure. These constraints may affect regulatory consistency, Shariah compliance, institutional competitiveness, and public confidence. The study therefore recommends strengthening national Shariah governance, adopting internationally recognized standards, institutionalizing independent Shariah auditing, improving tax neutrality, developing appropriate liquidity and capital-market instruments, and investing in specialized human capital. The paper’s core contribution is to synthesize Ethiopia’s evolving regulatory and Shariah governance landscape and pinpoint critical gaps between institutional expansion and governance development. These findings provide a basis for policymakers, regulators, and financial institutions to strengthen the sustainable development of interest-free finance in Ethiopia.

Abstract: Digital payment adoption is widely believed to broaden the visible tax base, and prior work links cashless payments to smaller value-added tax (VAT) compliance gaps. Whether this effect is large enough to shift a country's tax mix - the share of revenue from personal income tax (PIT), corporate income tax (CIT), and VAT - rather than raising each tax proportionately, remains untested. We examine this using a panel of 38 OECD countries, 2000-2022, instrumenting digital payment adoption with two time-invariant infrastructure-legacy proxies - broadband rollout timing and submarine cable distance - via two-stage least squares, benchmarked against naive OLS and two-way fixed effects. Naive OLS shows digital payment adoption significantly lowering the CIT and VAT shares of revenue, opposite to the base-broadening intuition; these associations vanish under instrumentation and fixed effects for every outcome. Weak-instrument-robust tests, a cluster bootstrap, and lagged and extended-control specifications corroborate the null, while a placebo test shows our instruments correlate with pre-sample tax composition, a genuine limitation we report in full. The two-way fixed-effects results, immune to that confound, corroborate the same null and are weighted as the most credible evidence. We conclude that digital payment adoption shows no robust relationship with OECD tax composition, consistent with mature tax administrations having already captured most realizable enforcement gains from digitalization before 2000.