Business, Economics and Management

Abstract: Most audit fee studies treat the relationship between fees and client risk as symmetric. A unit increase and a unit decrease in client risk are assumed to produce equal but opposite fee responses. We examine whether that assumption holds in the U.S. audit market using 4,090 firm-year observations of U.S. listed companies from 2010 to 2022 and a first-difference specification with firm and year fixed effects. The data show that audit fees rise by about 1.06 percent for each one-unit increase in the Audit Analytics Risky Client Score (p < 0.001). The response of fees to risk decreases is not statistically different from zero (coefficient = 0.001, p = 0.708). The implied stickiness differential is 0.0093 (p = 0.058). The stickiness ratio is approximately 0.13. Fees adjust downward at about 13 percent of the rate at which they adjust upward following an equivalent risk movement in the opposite direction. The pattern is robust to a strict definition of risk decreases, holds in both early (2010–2016) and late (2017–2022) sub-samples, and is corroborated by an alternative risk proxy based on loss-status transitions, where fees rise 4.3 percent on entry to loss status and do not adjust on exit. The result has implications for audit pricing models, audit committee oversight, and the way fee dynamics are interpreted by users of audit fee data.

Abstract: Accurate product-to-catalog invoice matching is a foundational internal control critical to financial oversight and audit quality, yet it is often bottlenecked by inconsistent vendor descriptions. Traditional rule-based matching fails to address this "long tail" of supplier heterogeneity, leading to costly manual reconciliation. This study presents an end-to-end system for automated invoice reconciliation. We introduce a novel “augment-both-sides” strategy: catalog entries are proactively enriched with LLM-generated keywords and synonyms before vectorization, while incoming invoice line items undergo query expansion to bridge the semantic gap between vendor terminology and master data. A final LLM-based reranker applies context-aware judgment to produce highly accurate Top-3 match candidates. We evaluate this system using three diverse entity resolution benchmark datasets, Abt-Buy, Amazon-Google and Walmart-Amazon, structured to simulate real-world ERP environments. The system achieves a Top-3 Recall of 93.14% to 97.96% across all domains, effectively narrowing the search space for accounting and auditing professionals from thousands of SKUs to a precise set of candidates. These results demonstrate that the architecture functions as a highly reliable intelligent decision aid, standardizing complex reconciliations, and structuring the reconciliation task for subsequent human verification.

Abstract: There are lots of perennial species that enable multiple harvests over years from one planting in nature. These crops require no repeated tillage and can promote root accumulation, thus leaving rural landowners with time for reproduction and further production, but this model is difficult for complex knowledge and operational difficulty. Focusing on the supplementation of distinctive species in rural household agriculture, this paper sorts out existing problems and compiles a biological resource list including perennial crops and self-reproducing animals. Combined with methods such as using bamboo trellises and other climbing structures to block light for non-crops, a household-based perennial agricultural scheme of "one-time work, continuous harvest" is constructed to ease reproductive pressure and accelerate civilizational development. Studies show that perennial, self-propagating, storable crops allow people to run a food company, avoid repetitive labor, and gain stable family food dividends; some resilient perennial species can gain competitive advantages with simple artificial tools, and combining the innate advantages of plants with the acquired strengths of tools can resist various risks; A diversified species lifespan table helps people plan investment according to species longevity and their own needs, allowing some species to form a cycle where longer lifespan is accompanied by larger root tubers and higher fruit yields.

Abstract: Following the global financial crisis, the transition to IFRS 9’s forward-looking Ex-pected Credit Loss (ECL) model has introduced significant implementation complexity, particularly in emerging markets facing data limitations. This study investigates the heterogeneous ECL compliance strategies adopted within the Cambodian banking sector during a period of heightened credit stress, marked by a system-wide non-performing loan ratio of 8.6%. Utilizing a multiple-case study design and replication logic, a quali-tative content analysis was conducted on the 2024 audited financial statements of 13 representative institutions, ranging from market leaders to international subsidiaries. The findings reveal a pronounced technical divide: market leaders utilize advanced internal statistical methods, such as cohort analysis, while international subsidiaries rely on top-down parent-group proxy models to bridge local data gaps. A “macro-correlation paradox” was identified, where certain institutions prioritize faithful representation by excluding macroeconomic variables when statistical links to historical defaults remain weak. Furthermore, a significant transparency gap exists, where granular disclosures are leveraged as strategic communication tools to signal institutional safety. These results suggest that ECL compliance in data-limited environments is a strategic management choice rather than a standardized technical exercise, highlighting the need for regulatory standardization of modeling assumptions to improve inter-bank comparability.

Abstract: This study set out to explore the role that microfinance institutions (MFIs) could play in promoting sustainable environmental practices within Zimbabwe’s arti-sanal gold mining sector. Despite the global economic benefits of the gold mining sector, it poses significant environmental challenges for Zimbabwe, including deforestation, water contamination, and soil erosion. The growing global focus on sustainability has prompted many questions about the role MFIs could play in fostering environmental responsiveness among enterprises in Zimbabwe. This study adopted an interpretivist research approach and a qualitative research design, conducting in-depth interviews with senior personnel from MFIs and artisanal gold miners in the critical gold mining areas of Shurugwi, Gwanda, Masvingo, and Kwekwe. Ten informed participants were purposively selected from these mining areas, and their opinion regarding the role that MFIs could play in promoting environmental responsiveness was examined. The study found that while MFIs' primary role and function is to focus on financial access and poverty reduction, they can also play an essential role in advancing sustainability initi-atives among community members in gold-mining areas. MFIs were found to have the potential to use monetary rewards to encourage environmentally friendly behaviours and to conduct ecological education among members of the gold mining community. However, challenges such as a weak regulatory framework and resource constraints were identified as major obstacles to MFIs' ability to promote environmental respon-siveness among members of gold-mining communities in Zimbabwe. The findings of this study underscore an essential, though underappreciated, role that MFIs could play in promoting environmental responsiveness in Zimbabwe's gold mining communities by incorporating environmental responsiveness into their mission statements. Accom-plishing this goal requires greater stakeholder cooperation and greater governmental oversight. The study advocates that the Zimbabwean government enact policies that integrate environmental management into MFIs' operations, offer incentives for micro-finance products, and create strategic partnerships to deliver technical support and environmental management education to the artisanal gold mining sector to curb growing environmental degradation in Zimbabwe. In addition, it is recommended that enhancing environmental regulatory supervision and creating reliable monitoring mechanisms are essential for preserving the environment for future generations.

Abstract: This study investigates the impact of ESG performance on the tax avoidance behavior of 118 non-financial listed firms in Vietnam from 2020 to 2024. Employing a Random Effects Model (REM), empirical results reveal that sustainability reporting quality-measured by individual E, S, and G pillars and a composite ESG index-is negatively associated with corporate tax avoidance, proxied by the Effective Tax Rate (ETR). Among these, the social (S) pillar exerts the most pronounced effect; however, individual component impacts remain less substantial than the comprehensive ESG index. Furthermore, findings indicate that the mitigating effect of ESG on tax avoidance significantly weakens when firms face financial constraints or operate under state ownership. Notably, applying machine learning techniques demonstrates that a CatBoost algorithm integrating the ESG variable achieves 52.92% predictive accuracy for tax avoidance, outperforming an XGBoost model lacking ESG inclusion (38.14%). Additionally, feature importance analysis of financial and non-financial variables highlights ROA as the dominant financial predictor (35.5%), while ESG contributes a notable 10.35% to the model's explanatory power. Ultimately, these findings provide vital insights for policymakers and investors regarding the interplay between sustainability commitments, ownership structures, and corporate tax strategies.

Abstract: This study examines how digitalization can reduce the cost and complexity of ESG and circular economy reporting for women-led SMEs within the evolving EU sustainability reporting framework, with a focus on the Danube Region. Using a conceptual accounting approach grounded in EU regulatory documents, academic literature, and prior bibliometric research, it identifies four key challenge do-mains: measurement, valuation, disclosure, and professional judgment. The analysis is complemented by an exploratory empirical extension based on publicly available documents and illustrative cases of women-led SMEs from the Danube Region. It develops an accounting-oriented problem matrix linking these challenges to digital enablers such as data platforms, automation tools, and traceability technologies. The findings suggest that digitalization improves not only efficiency, but also the reliability, auditability, comparability, and scalability of ESG reporting. A conceptual framework is proposed, connecting regulatory drivers, digital accounting capabilities, and reporting outcomes, including improved assurance readiness and access to finance. The paper also provides practical recommendations, including minimum viable ESG datasets and a staged digital adoption approach, alongside policy implications related to harmonized data requests and targeted capacity-building for SMEs. The study contributes by integrating ESG reporting, circular economy, digitalization, and gender-sensitive SME constraints into an explicitly accounting-centered analytical framework.

Abstract: Small and medium-sized enterprises are prone to errors or evasion in areas such as medical insurance reimbursement, pension contributions, and unemployment insurance, which not only harm workers' rights but also impact the sustainability of federal and state public funds. Therefore, an explainable intelligent tax compliance screening system is urgently needed. This paper uses multi-task learning as the backbone, while modeling the shared risk representations of sub-tasks such as wages, pensions, unemployment insurance, and medical insurance contributions. Subsequently, our model combines multi-task learning with time-series anomaly detection to capture overlooked scenarios and structural avoidance in long-term trends. On heterogeneous time-series graphs, SHapley Additive exPlanations (SHAP) contributions are propagated and decomposed according to 'feature-module-time-task,' and combined with gated dependencies and consistency constraints to generate auditable risk paths, achieving an upgrade from anomaly scoring to evidence-chain visualization. Experiments show that this method improves both identification performance and interpretable localization efficiency.

Abstract: This study examines the role of Enterprise Risk Management (ERM) in mitigating cyber fraud in Indonesian State-Owned Enterprises (SOEs). As digital transformation increases organizational exposure to cyber risks, effective risk governance mechanisms become essential for safeguarding financial integrity. This research investigates how ERM implementation contributes to cyber fraud prevention and detection within SOEs. The study employs a mixed-methods approach using quantitative panel data from 48 non-financial SOEs during the 2020–2024 period, resulting in 112 firm-year observations, complemented by qualitative insights from 25 key informants, including auditors, risk officers, and IT/cybersecurity specialists. The empirical analysis indicates that stronger ERM implementation significantly enhances firms’ ability to mitigate cyber fraud risks and improves coordination between financial risk management and information technology governance. The findings also highlight the importance of integrated risk governance structures in strengthening internal controls and organizational resilience against digital threats. This study contributes to the literature on risk governance and digital risk management by providing empirical evidence on the strategic role of ERM in enhancing financial accountability and cyber risk mitigation in emerging market SOEs.

Abstract: This study evaluates the extent and quality of tax transparency reporting among the Top 40 firms listed on the Johannesburg Stock Exchange (JSE), distinguishing between mandatory tax disclosures and voluntary transparency practices. A qualitative, disclosure-based research design was employed, involving content analysis of publicly available annual reports, integrated reports, and sustainability reports. A structured tax transparency framework grounded in stakeholder theory and legitimacy theory, and adapted from prior empirical studies was applied to systematically assess tax-related disclosures. Findings indicate high compliance with mandatory tax disclosure requirements, reflecting strong adherence to accounting standards and regulatory obligations. In contrast, voluntary tax transparency shows considerable variation: firms predominantly provide narrative, policy-oriented, and governance-related information, while detailed, forward-looking, and jurisdiction-specific disclosures remain limited. The discussion highlights that voluntary transparency is shaped by stakeholder expectations, legitimacy concerns, and perceived reputational and commercial risks, leading to selective disclosure. Regulatory compliance emerges as the primary driver of tax reporting, whereas voluntary practices are influenced by firm-specific and contextual factors. The results hold relevance for investors, regulators, and policymakers seeking greater corporate accountability, and for standard-setters aiming to enhance the consistency and depth of tax transparency reporting. Overall, the study enriches the limited literature on corporate tax transparency in emerging markets by offering contemporary empirical evidence from South Africa and identifying key areas requiring improvement in voluntary tax disclosures.

Abstract: Artificial intelligence (AI) is rapidly reshaping transfer pricing (TP) practice and enforcement. Tax authorities increasingly use AI-driven risk models for audit selection, while multinational enterprises apply machine learning to automate benchmarking, documentation, and operational TP monitoring. This Article reviews the emerging literature and legal context to assess a key question: is AI in TP a risk or an opportunity? Drawing on OECD reporting, peer-reviewed studies, and practitioner and regulatory materials, the Article identifies measurable opportunities, including faster benchmarking and documentation, predictive compliance controls, and more consistent application of TP policies across jurisdictions. It also isolates material risks: opaque "black box" outputs, data and algorithmic bias, unsettled regulatory treatment under the EU AI Act and data protection law, and expanding litigation and disclosure disputes. The Article concludes that AI is a net opportunity for TP governance only when deployed as decision support under robust human-in-the-loop safeguards, with explainability, audit trails, and periodic bias review sufficient to preserve the arm's length standard and procedural fairness.

Abstract: This study examines the relationship between board composition and integrated reporting quality (IRQ) and tests whether firm performance mediates this relationship. Using content analysis, IRQ is measured through a disclosure index based on the 2021 International Integrated Reporting Framework. The sample comprises 550 integrated annual reports from 110 companies listed on the Johannesburg Stock Exchange across multiple sectors for the period 2020–2024. Panel multiple regression analysis reveals that board size and gender diversity are positively and significantly associated with IRQ, suggesting that larger and more gender-diverse boards enhance monitoring effectiveness and stakeholder-oriented transparency. In contrast, board independence and audit committee size show no significant association with IRQ. The findings further indicate that firm performance does not mediate the relationship between board composition and IRQ, implying that reporting quality is driven more by governance structures than by financial outcomes. This study contributes post-2021 Framework evidence from a mandatory integrated reporting context, refines governance theory by highlighting the importance of board heterogeneity over formal independence, and positions IRQ as a governance outcome rather than a financial signalling mechanism. The results offer practical insights for regulators, investors, and policymakers seeking to enhance reporting quality.

Abstract: Water-related resources are increasingly critical to corporate sustainability, yet many businesses struggle to account for their marine impacts effectively. Blue accounting has emerged as a tool to improve corporate transparency and responsible water stewardship, especially concerning marine ecosystems. This study conducts a scoping review to identify factors influencing corporations' adoption of blue accounting disclosures. Following Arksey and O’Malley’s framework, 109 studies were initially screened, with 18 peer-reviewed articles selected for final analysis. Results reveal that stakeholder pressure, climate action, competitive positioning, and alignment with sustainability goals, especially SDG 14, are key enablers of adoption. However, major barriers include the lack of a standardised reporting framework, limited regulatory mandates, and institutional and technical capacity constraints. The review highlights the strategic use of blue accounting as a signalling mechanism, while also cautioning against superficial “blue-washing.” It concludes that targeted policy interventions, capacity building, and globally harmonised standards are essential for improving disclosure quality and corporate accountability in marine sustainability. This review contributes to the literature by mapping current evidence, identifying adoption drivers and barriers, and proposing directions for empirical research and policy development.

Abstract: Prior research has shown that security breaches are associated with an increase in auditor fees (Smith, Higgs, and Pinsker, 2019). Auditors serve an important role in external governance with respect to a firm’s overall risk management protocol. Cybersecurity insurance provides financial protection against unexpected security breaches, helping businesses manage risk that could otherwise lead to significant financial hardship. Our study examines the role cybersecurity insurance plays in mitigating the breach risk audit fee premium. Using a sample of firms from 2008-2018, we explore how auditors view audit risk related to breach risk and whether cybersecurity insurance mitigates this risk premium. Our evidence suggests that the purchase of cyber insurance results in higher audit fees, but that coupling cyber risk oversight within the audit committee with the acquisition of cyber insurance reduces auditors’ perceptions of risk in post breach periods. These results should provide guidance to firms and regulators as they seek to address and mitigate the business risks associated with information technology.

Abstract: Environmental, Social and Governance (ESG) risk is increasingly influenced by inter-firm relationships embedded in global supply chains. Challenging firm-level approaches that treat ESG exposure as independent across companies. This study examines whether firms’ structural positions within supply-chain networks are associated with ESG risk exposure and whether incorporating network information improves ESG risk prediction. The analysis draws on an international dataset integrating validated supplier-buyer relationships, shipment-level trade data. ESG incident records and sentiment derived from ESG-related news. Network-based econometric models and graph-oriented learning approaches are evaluated against conventional firm-level benchmarks. The results indicate that ESG risk clusters within connected groups of firms, with higher exposure observed among firms occupying central or intermediary positions in supply networks. In addition, ESG-related media sentiment exhibits predictive power for subsequent ESG incidents, supporting its role as an early warning signal. Overall, models that explicitly account for network structure deliver more accurate and better-calibrated predictions than standard econometric and machine-learning approaches. These findings highlight the value of a network-informed perspective for ESG risk assessment in complex international production systems.

Abstract: Ensuring consistency, fairness, and transparency in cross-regulatory compliance has become a critical national priority as enterprises increasingly file interdependent reports to agencies such as the IRS, SEC, and DOL. However, fragmented regulatory ecosystems often lead to inconsistent filings, elevated fraud risk, and inefficient allocation of audit resources. To address these challenges, this paper proposes a unified audit-intelligence framework that integrates neuro-symbolic reasoning, blockchain-based trust management, and deep reinforcement learning within a multi-agent system. First, a neuro-symbolic consistency engine combines graph neural networks with first-order logical rules to detect subtle, cross-form discrepancies that may indicate misreporting or fraud. Second, all generated evidence trails and verification outcomes are anchored on a permissioned blockchain to ensure tamper-proof traceability and transparent regulatory collaboration. Third, a multi-agent deep reinforcement learning module dynamically allocates IRS audit resources by jointly optimizing long-term tax recovery and fairness objectives, mitigating disproportionate enforcement on disadvantaged groups or small businesses. Experimental simulations using synthetic and semi-real regulatory datasets demonstrate that the proposed system significantly improves cross-agency report consistency detection (+21.7%), enhances audit transparency, and reduces allocation bias by up to 34%. This research provides a technologically grounded pathway for modernizing regulatory intelligence, safeguarding tax bases, and supporting equitable and sustainable compliance enforcement in the United States.

Abstract: This study presents the problem of corruption in the context of the forestry industry in Chile, focusing its analysis on two companies in the sector. In this regard, there have been some cases of corruption that have affected their reputation, causing damage to their governance. The objective focuses on analyzing the anti-corruption mechanisms and policies implemented by these companies, as disclosed in their sustainability re-ports, considering the law on crime prevention, risk management, and existing control mechanisms. The results reveal significant differences in levels of transparency and implementation: CMPC shows more comprehensive disclosure in its discourse, struc-ture, and mechanisms, while Arauco reveals a more formalized structure and organi-zation, with a focus on control and compliance. However, the conclusions indicate the need to strengthen regulatory frameworks to move toward effective transparency, considering that they reveal a gap between what is applied and what is said.

Abstract: Blockchain is widely promoted as a tool for enhancing transparency, trust, and sustainability in business, yet little is known about how creative micro, small, and medium enterprises (MSMEs) in emerging economies can meaningfully adopt it for finance and accounting purposes in times of global uncertainty. This study explores how blockchain can be harnessed for transparent and sustainable accounting in Indonesian creative MSMEs amid rapid digital disruption. Using an exploratory qualitative design, we conducted semi-structured, in-depth interviews with 18 owners and key decision-makers across diverse creative subsectors and analysed the data thematically through an integrated Technology Acceptance Model (TAM) and Diffusion of Innovation (DOI) lens. The findings show that participants recognise blockchain’s potential benefits for transaction transparency, verifiable records, intellectual property protection, and secure payments, but adoption is constrained by technical complexity, financial constraints, limited digital and accounting capabilities, and perceived regulatory and reputational risks. Government initiatives are seen as important for legitimacy yet insufficient without concrete guidance, capacity-building, and financial support. The study extends TAM–DOI applications to blockchain-enabled accounting in creative MSMEs and highlights the need for sequenced, ecosystem-based interventions to translate blockchain’s technical promise into accessible, ESG- and SDG-oriented accounting solutions in the creative economy.

Abstract: There are indices for measuring the different aspects of how companies disseminate information, but none specifically assesses the level of blockchain technology adoption by financial institutions. Given the rapid development and applications of blockchain technology in finance, evaluating its adoption by financial institutions is crucial. In this context, this study develops and tests a formative blockchain technology adoption index to quantify the extent of blockchain adoption in the financial sector, based on data from the annual reports of leading banks in Africa. The research employs a triangulation methodology, which involves identifying indicators that can be combined to form a blockchain technology adoption index. These indicators were evaluated against the SMART criteria for selecting effective metrics. The index was then created based on these indicators and applied using data from 20 leading banks in Africa. These banks were chosen for their regional representation, size, and rankings derived from Africa Business data, a reputable source that uses Tier 1 capital as a key indicator of bank strength. Findings revealed that banks in West Africa lead with the highest adoption score of 72%, followed by South Africa at 66%. Furthermore, an ANOVA test at a 0.05 confidence level was used to examine whether bank size significantly influences blockchain adoption levels. The results showed no statistical difference in the average total assets between larger and smaller banks in the sample, thereby indicating the index’s broad applicability regardless of bank size. This study concludes that the developed blockchain technology adoption index in this study can be applied to banks and other financial sector companies of varying revenue sizes across different regions in Africa and beyond.

Abstract: Artificial intelligence (AI) is transforming accounting by automating cognitive tasks and redefining mechanisms of governance and risk control. This study examines how AI operates as an intelligent control system—one that substitutes manual accounting procedures while enhancing transparency, internal control, and fraud detection. Inte-grating the Technology Acceptance Model (TAM) with Organizational Information Processing Theory (OIPT), the research develops a behavioral–organizational frame-work linking perceived usefulness, ease of use, AI literacy, technology readiness, social influence, and facilitating conditions to AI adoption and perceived substitution bene-fits. A structured survey was administered to accounting students and practitioners in Northern Italy (n = 185) and analyzed through reliability tests and partial least squares structural equation modeling (PLS-SEM). The results show that AI literacy, facilitating conditions, and social influence significantly drive adoption intention, while perceived substitution benefits fully mediate the relationship between adoption and governance outcomes. The findings demonstrate that AI adoption enhances governance and risk-management effectiveness by functioning as an intelligent control mechanism. The study introduces the AI-to-Control (A2C) Blueprint to guide responsible integra-tion of AI into accounting systems, reframing AI adoption as a structural evolution in corporate governance rather than a mere technological upgrade.