Business, Economics and Management

Abstract: The deployment of Sustainability Balanced Scorecard (SBSC) systems in developing economies confronts institutional voids and structural frictions that depart from the assumptions embedded in developed-market models. This study develops and validates a context-specific SBSC evaluation architecture for the Iraqi cement manufacturing sector by combining Glaserian grounded theory with a hybrid Fuzzy Multi-Criteria Decision-Making (MCDM) framework comprising Fuzzy Delphi, Fuzzy DEMATEL, and behavioral Fuzzy TODIM. Drawing on semi-structured interviews with fifteen senior executives and sustainability experts from cement producers listed on the Iraq Stock Exchange, four core dimensions were established: concept and objectives of sustainable development (D1), SBSC alignment with organizational sustainability values (D2), sustainability performance data collection and monitoring (D3), and implementation challenges (D4). Fuzzy DEMATEL and TODIM computations reveal that implementation challenges (D4) and strategic objectives (D1) act as primary causal drivers, whereas data monitoring (D3) and scorecard alignment (D2) operate as downstream effects. These results challenge the conventional managerial premise that sustainability measurement failures stem primarily from metrics design or software deficits, indicating instead that institutional readiness, governance quality, and managerial loss aversion govern execution. A phased operational roadmap is offered for corporate decision-makers and regulators in transitional economies.

Abstract: Financial statements are prepared from trial balances that have passed internal-control checks and, for external reporting, have been audited before stakeholders rely on them. Converting this verified ledger into the views needed by investors, creditors, regulators, and management remains largely manual, causing rework, delays, and duplicated reconciliation. This study designs and evaluates an artificial intelligence (AI)-coordinated architecture that generates stakeholder-specific yet reconciled reports from a single governed trial balance. Following a design science approach, the architecture combines centrally configured accounting policies and KPI definitions, lineage metadata, account classification, anomaly detection, sequence forecasting, financial natural language processing (NLP), role-aware customization, and human-in-the-loop explainable AI (XAI). The prototype was evaluated on 15,000 trial balances for 500 entities (12,000 synthetic and 3,000 derived from public SEC filings) against rule-based, robotic process automation (RPA), and commercial generative-AI baselines. It achieved a line-item disclosure fidelity (F1) of 0.965, an AUC of 0.97 for misclassification detection, and a mean absolute percentage error (MAPE) of 4.8% for one- to four-quarter forecasts; in 250 simulated multi-entity scenarios, preparer effort fell from 38.0 to 4.2 person-hours per reporting cycle. Simulated stakeholder and auditor responses illustrate the expected gains in perceived usefulness and trust. Limitations include synthetic and statement-derived data, simulated rather than observed perception measures, and an unevaluated narrative-generation module.

Abstract: This paper examines when disclosure helps investors value intangible investments and what information is useful under different forms of uncertainty. We connect the disclosure literature with the theories of decision-making under ambiguity and real options. On this basis, we develop a conceptual framework that traces what has been invested, what value is expected, and what results have been achieved. We distinguish information asymmetry, where management knows more than the investor, from shared uncertainty about future outcomes. Shared uncertainty takes the form of risk when probabilities can be estimated, or ambiguity when there is no reliable basis for estimating them. Under shared ambiguity, information about management’s future decisions may be more useful than a detailed forecast of the final outcome. This information includes the project’s stages, the funding required, and the conditions for continuation or termination. Disclosing this information enables investors to account for opportunities to limit future losses. Its usefulness depends on how reliably the value and costs associated with termination can be assessed and whether management will follow the announced criteria. Subsequently comparing actual decisions with these criteria, including for unsuccessful projects, may reveal more about management’s reliability than the accuracy of a single forecast. The framework also explains why creditors and shareholders have different information needs. We formulate five propositions for future empirical testing and illustrate their conditions with numerical examples. The analysis offers guidance for disclosure on intangible investments in the context of the review of IAS 38.

Abstract: A useful reading of supplier finance disclosures keeps three things apart: how much of an arrangement a buyer discloses, how much suppliers have been paid, and the buyer's own payment terms. Part of that separation is arithmetic — the financed-to-payables ratio equals the paid share of coverage times the coverage-to-payables ratio, so substituting total coverage for paid financing mechanically inflates any exposure measure. The contribution is empirical: comparing 2024–2025 consolidated reports from five European consumer groups (Carlsberg, Haleon, Heineken, Nestlé, Unilever) shows where these identities meet disclosure practice in ways the algebra alone would not predict. In 2025 the paid share of covered liabilities ranged from 54.5% at Haleon to 85.0% at Carlsberg; scaling by trade payables flipped the Heineken–Unilever ranking; Heineken disclosed an identical 7–180-day range for arrangement and comparator payables despite an 8–11-day gap between weighted averages; and Nestlé's buckets showed roughly 53 percentage points more balances beyond 120 days inside the arrangement than outside it. A comparability check finds one company's ratio numerator and denominator not drawn from the same accounting population. The findings support a defensible, stepwise reading without claiming non-compliance, forecasting a liquidity shortfall, or generalising beyond the five cases examined.

Abstract: Generative artificial intelligence (GenAI) is increasingly used in corporate reporting, where accounting policies and estimates require professional judgment. This conceptual study examines how GenAI may assist preparers without displacing human responsibility or reinforcing managerial bias. Using theory adaptation, it integrates research on professional judgment, motivated reasoning and automation reliance. It identifies five bias-amplification mechanisms: anchoring, automation bias, outcome-directed prompting, unverified justification and diffusion of responsibility. The paper develops TRACE, a proposed framework comprising task boundary, regulatory grounding, alternative assessment, challenge and contradiction, and evidence trail. Its intended operation is illustrated through four constructed IFRS cases covering IAS 8 and IAS 16, IAS 36, IAS 37 and IFRS 15, three involving climate-transition assumptions. The AI proposals are stylised illustrations, not actual model outputs or empirical data. The analysis suggests that GenAI is most defensible when used to identify alternatives and contradictory evidence and to challenge a preferred treatment, and least defensible when asked to select or justify a conclusion. TRACE may make AI-assisted judgments more attributable and contestable, but it cannot guarantee unbiased or technically correct outcomes; independent human review remains necessary. The framework has not been empirically validated. It considers governance, control, reporting reliability and sustainable management.

Abstract: This study examines accounting connectivity between FY2024 sustainability statements prepared under the European Sustainability Reporting Standards (ESRS) and corresponding IFRS financial statements. Using the full eligible population of 22 Portuguese companies listed on the Euronext Lisbon regulated market, it develops and applies a 26-item Accounting Connectivity Index (ACI) across four dimensions: Referential, Quantitative, Methodological, and Accounting Consequence Connectivity. The analysis combines structured content analysis with qualitative assessment of sustainability-to-accounting pathways. ACI scores range from 17.1 to 94.8, with a population mean of 57.1. Quantitative Connectivity is the strongest and most widespread dimension, largely reflecting traceable relationships between EU Taxonomy metrics and financial-statement amounts. Accounting Consequence Connectivity, by contrast, is concentrated in seven companies. The results show that quantitative traceability does not necessarily imply substantive accounting connectivity and support a distinction between disclosure connectivity and integration into accounting assumptions, estimates, measurement, and financial-statement outcomes. Connectivity pathways also differ across business models. The findings remain stable under alternative population definitions, item specifications, and weighting approaches. The study contributes a replicable, sector-neutral framework for evaluating the nature and depth of connectivity between sustainability and financial reporting.

Abstract: This study investigates the effect of ownership structure on real earning management (REM) of food and beverage (F&B) companies listed on the Vietnam stock market, with particular emphasis on the moderating role of auditing quality. The data employed in this study includes 138 listed F&B companies covering the period from 2022 to 2024. Using a feasible generalized least squares (FGLS) approach, the empirical findings confirm that foreign ownership and managerial ownership have significantly negative effects on earning management of the food and beverage companies. However, this study does not find any evidence on the effect of the state ownership on the earning management. Additionally, the results indicate that higher audit quality, as proxied by Big 4 auditors, is associated with lower earnings management. Especially, the findings show positive moderating effects of the Big 4 auditors on the relationship between ownership structure and earnings management, meaning that the presence of a Big 4 auditor weakens the marginal impact of ownership structure on the earnings management. This evidence supports the view that governance mechanisms can act as substitutes rather than complements in constraining real earning management, contributing to the broader literature on corporate governance in emerging markets. This study provides the first empirical evidence on how ownership structure influences REM within Vietnamese F&B companies. Moreover, the study offers insights into the moderating role of audit quality in the relationship between ownership structure and REM, thereby enhancing understanding of whether governance mechanisms act as substitutes or complements in constraining opportunistic behavior within transitional economies.

Abstract: This study examines whether firm-level climate risk exposure is associated with accounting transparency using a global panel of firm-year observations spanning 91 countries. Corporate climate risk exposure is measured using earnings-call text-based indicators developed by Sautner et al. (2023), while accounting transparency is captured through three proxies: absolute accruals, earnings smoothing ratio, and earnings smoothing correlation. The results show that climate risk exposure is negatively and significantly associated with absolute accruals, indicating that climate-exposed firms exhibit lower accrual-based opacity and therefore higher accrual-based accounting transparency. The effect is economically meaningful: a one-standard-deviation increase in climate risk exposure is associated with a 13.2% decline in absolute accruals relative to the sample mean. This finding is robust to entropy balancing, dynamic system generalized method of moments (GMM) estimation, and an alternative climate exposure proxy. Environmental, social, and governance (ESG) channel analyses further show that the negative association between climate risk exposure and absolute accruals is stronger among firms with above-median social, environmental, and governance scores, suggesting that ESG performance strengthens the transparency response to climate-related uncertainty. By contrast, climate risk exposure is not significantly associated with earnings smoothing ratio or earnings smoothing correlation across ESG groups, indicating that the effect is concentrated in the accrual-based channel rather than in smoothing-based reporting behaviour. The study contributes to the climate-finance literature and the accounting literature by linking firm-level climate risk exposure to financial reporting quality and by identifying ESG performance as an important conditioning mechanism. The findings are consistent with information asymmetry and signalling perspectives and carry practical implications for regulators, standard-setters, auditors, boards, and investors.

Abstract: Purpose: This paper examines what predicts digital financial inclusion among already high-income, well-governed countries, reversing the usual causal framing that treats inclusion as a driver rather than an outcome of institutional quality, financial-sector development, and digital infrastructure legacy. Design/methodology/approach: Using a panel of 38 OECD countries (2000-2022, 874 country-years), account ownership and digital payment use are modeled as fractional response variables decomposed into within- and between-country components (Bell and Jones, 2015), with Tobit and Worldwide Governance Indicator cross-sectional robustness checks. Findings: Government effectiveness predicts inclusion almost entirely through persistent between-country differences, not within-country governance change. Early broadband rollout and submarine cable proximity independently predict higher digital payment use. A naive two-way fixed-effects specification erases the institutional relationship entirely. Rule of law and regulatory quality, not political stability, carry the institutional effect. Research limitations/implications: The decomposition establishes association, not causation; the governance-dimension check is cross-sectional rather than a full panel. Practical implications: OECD and accession-track governments should prioritize digital-payment infrastructure over governance reform as a short-run inclusion strategy, while sustaining rule-of-law investment for its structural payoff. Originality/value: The paper reverses the standard causal framing in the digital financial inclusion literature and combines five data sources within a single OECD panel not previously analyzed together.

Abstract: The increasing complexity of organizational operations, digital transfor-mation, and growing sustainability requirements have significantly expanded the scope of auditing beyond traditional financial assurance functions. Modern or-ganizations require comprehensive audit approaches capable of evaluating busi-ness process efficiency, risk management effectiveness, internal control quality, and corporate governance performance. However, existing audit effectiveness assessment frameworks primarily focus on financial reporting and compliance outcomes, providing limited insight into the overall effectiveness of business process auditing. This study aims to develop and validate an Integrated Business Process Audit Effectiveness Index (BPAEI) for joint-stock companies by incor-porating key operational, risk-oriented, and governance-related dimensions of auditing. The research adopts a quantitative approach based on survey data col-lected from auditors, internal control specialists, and corporate governance pro-fessionals. The proposed BPAEI model consists of five core determinants: Audit Coverage Rate (CR), Risk Detection Rate (RDR), Internal Control Index (ICI), Implementation Rate of Audit Recommendations (IR), and Process Audit Index (PAI). Structural Equation Modeling (SEM) using SmartPLS is employed to examine the relationships between these determinants and overall business pro-cess audit effectiveness. The empirical findings indicate that all five factors have a positive and statistically significant impact on audit effectiveness. Among them, Risk Detection Rate and Internal Control Index demonstrate the strongest influence, highlighting the importance of risk-oriented auditing and effective internal control systems in enhancing organizational performance. The proposed index exhibits satisfactory reliability and validity, confirming its applicability as a comprehensive measurement tool for evaluating business process audit effec-tiveness. This study contributes to the auditing literature by introducing a novel multidimensional framework that integrates operational auditing, risk manage-ment, internal controls, and governance considerations into a single quantitative model. The findings provide practical implications for auditors, regulators, and corporate managers seeking to improve audit quality, strengthen corporate gov-ernance, and support sustainable business development. The BPAEI framework can serve as a valuable instrument for assessing audit performance, benchmarking organizational practices, and facilitating evidence-based decision-making in both emerging and developed economies.

Abstract: Korean listed companies report business promotion expense, the cost of entertaining customers and counterparties, as a separate account, and the COVID-19 pandemic interrupted the activity that account pays for. This study asks whether the stock market’s valuation of that spending changed around the interruption. Using 19,334 firm-years on 2,331 Korean listed firms over 2016 to 2025, Tobin’s Q is regressed on business promotion expenditure scaled by sales, interacted with indicators for the pandemic years 2020 to 2021 and the years that followed, with firm and year fixed effects and standard errors clustered by firm. Entertainment intensity was positively associated with firm value before the pandemic, and the association during the pandemic was statistically indistinguishable from that benchmark. After the pandemic the association was eliminated: the interaction is −29.655 and the post-pandemic slope is no longer distinguishable from zero. Year-by-year estimates place the change in 2022 rather than 2020. The share of firms reporting no entertainment expenditure was flat before 2020 and has risen by about one percentage point a year since, and the market prices the amount spent rather than the decision to spend at all. The pandemic did not change how this spending was valued; what followed it did.

Abstract: Environmental taxation is an important instrument in the transition to a low-carbon economy, yet empirical findings on its relationship with economic growth remain inconclusive. This study examines whether public debt moderates the relationship between environmental tax revenue and real GDP growth in the EU-27. It uses a balanced panel of 297 observations covering 2014–2024. The baseline specification is a two-way fixed-effects model with Driscoll–Kraay standard errors, supplemented by marginal-effects analysis and robustness checks. At the mean level of the other interacting variable, neither environmental tax revenue nor public debt exhibits a statistically significant conditional association with economic growth. However, the interaction coefficient is negative and statistically significant, indicating that the estimated relationship between environmental tax revenue and growth becomes less favourable as public debt increases. The marginal effect is positive and statistically significant at low debt levels, statistically insignificant at the mean debt level, and negative but statistically insignificant at high debt levels. Robustness checks preserve the negative sign of the interaction, although its statistical significance depends on the covariance estimator used. The findings indicate that fiscal conditions matter for the environmental taxation–growth relationship, but they do not establish a causal effect or a universal debt threshold.

Abstract: The European Union has set the target of doubling the share of secondary materials in overall consumption by 2030. Nevertheless, in 2024 this share reached only 12.2%, while the environmental tax systems of the member states remain dominated by energy excise duties. Against this background, the article investigates whether the size, structure, temporal profile and territorial distribution of environmental taxation matter for the circular transition. The study uses a balanced panel of the 27 EU member states over the period 2010–2024. For the economic indicators of the circular sectors, the period 2005–2023 is used. Circular outcomes are measured through five indicators from the EU's official monitoring framework, with the database constructed entirely from Eurostat sources. The methodology combines two-way fixed-effects models with clustered standard errors, lag specifications, two-step system GMM, and models with interactions and conditional marginal effects. The results show that the overall environmental tax burden is not systematically associated with circular outcomes. When the aggregate indicator is decomposed, however, divergent effects emerge. Energy taxes are associated with lower private investment in the circular sectors, whereas pollution taxes display a positive relationship with their value added. Resource taxes remain too limited as a fiscal share for their effect to be reliably estimated. The dynamic analysis shows that the fiscal effects manifest with a lag of around two years. Furthermore, they are concentrated mainly in the weakly agricultural economies and weaken as agricultural specialisation increases. Consequently, uniform green fiscal instruments do not lead to identical circular outcomes across all member states. The findings support the need to reorient taxation towards pollution and resources, a compensatory design for the energy-intensive circular industries, and territorial differentiation through the targeted use of revenues in rural areas.

Abstract: The mining industry remains one of the most hazardous industries globally, with oc-cupational injuries and fatalities imposing significant operational and financial costs on companies. Although prior research has explored the impact of workplace safety on fi-nancial performance, limited attention has been devoted the role of board gender di-versity in influencing this relationship. This paper examines the association between safety performance and firm financial performance in 46 JSE-listed mining firms, con-sidering moderating role of board gender diversity. The study adopted unbalanced panel dataset covering the period 2015 to 2025. Safety performance data was obtained from sustainability reports and integrated reports, while financial performance and board gender diversity were obtained from annual financial reports. The fixed effects model was adopted to establish the relationship between study variables, while the two-step system generalised method of moments was used to as robustness test. The study found a significant negative association between injury frequency rate and financial perfor-mance of JSE-listed mining firms, whereas fatality frequency rate shows insignificant association with financial performance. The findings show that board gender diversity shapes safety performance-firm financial performance relationship. The study contrib-utes to occupational safety and corporate governance literature by providing evidence on the governance conditions under which safety performance improve financial per-formance of South African mining sector. The study’s results suggest that JSE-listed mining firms should improve occupational safety programs and promote greater women representation in boards to improve long-tern financial performance.

Abstract: Innovations in artificial intelligence are reshaping how tax administrations approach compliance and audit planning, yet existing AI-based fraud detection studies largely treat the taxpayer population as homogeneous or remain conceptual frameworks awaiting empirical validation. This gap is consequential because audit resources are limited, evasion tactics are increasingly sophisticated, and misallocating scarce audit capacity carries a direct fiscal cost. To address it, this study presents VERITAS, a machine learning-based decision support system operationalizing a segment- and sector-aware architecture for corporate income tax audit planning: a single-layer model for Large Taxpayer case selection, and a novel two-layered model for small and medium enterprises (SMEs) that filters evasion-suspect cases before prioritizing them by expected tax-recovery yield against a target threshold. Ten classification algorithms were compared across 4,063 SME and 1,903 Large Taxpayer financial statements, with correlation-ranked feature selection subsequently applied to each. Random Forest consistently outperformed all alternatives across every segment, sector, and task examined; feature selection improved performance in every case; sector-specific modeling outperformed a generic classifier in three of four SME sec-tors; and a novel business-activity-code feature was retained in most analyses. These findings position VERITAS as a practical innovation in tax audit practice: a deployable, generalizable template for AI-driven audit planning built entirely from data tax administrations already collect.

Abstract: This study examines the primary factors influencing audit fees among Russell 3000 businesses from 2001 to 2023. The impact of market indices and the existence of key audit matters (KAMs) on audit fees are particularly emphasized to offer an enhanced understanding of audit cost structures. The study employed a panel data analysis with econometric models to measure the correlation between audit fees and other independent variables, using data obtained from the Audit Analytics database, encompassing financial and audit-related factors for firms within the Russell 3000 index, excluding the financial sector. This analysis revealed that assets, earnings, and revenue are the primary predictors of audit fees, although book value exerts no substantial influence due to the counteracting impacts of positive and negative values. These findings facilitate auditors and companies’ improved understanding of audit pricing structures and, support better-informed negotiations and resource planning in audit engagements.

Abstract: Digitalisation is increasingly viewed as an important instrument for narrowing the VAT gap in the European Union, yet it remains unclear which forms of digitalisation are most closely associated with improved collection. This study distinguishes between two channels: the voluntary adoption of cloud accounting by firms and the mandatory digital reporting of transactions to tax administrations. The analysis uses an unbalanced panel for the EU-27 covering 2013–2024, with the main econometric estimations restricted to 2013–2023. Data from the European Commission, Eurostat and the World Bank are examined through sequential pooled OLS specifications, two-way fixed-effects models and robustness checks. The initially negative association between cloud accounting and the VAT gap disappears after income and government effectiveness are controlled for. Mandatory digital reporting, by contrast, is associated with a VAT gap approximately 3–4 percentage points lower across the main specifications. However, the limited number of adopting countries and the presence of a pre-adoption downward trend restrict causal interpretation. The findings suggest that digitalisation is more closely related to VAT collection when it provides tax administrations with timely, structured and verifiable transaction data. Voluntary business digitalisation may support compliance, but it does not substitute for mandatory administrative reporting.

Abstract: Tax administrations increasingly rely on data-driven risk models to prioritize enforcement and outreach resources, yet machine learning applications to personal income tax arrears remain scarce compared with corporate-tax and fraud-detection contexts. This study introduces TaxMind, a machine learning framework for predicting tax payment arrears, operationalized as escalation to mandatory (forced) collection, comparatively evaluating five classifiers and applying Shapley-value techniques to interpret the resulting model. A dataset of 16,010 administrative tax records was cleaned by removing 5,385 exact duplicate rows (33.6% of raw data), yielding 10,625 records across 9,264 taxpayers; a taxpayer-grouped train/test split prevented information leakage between related observations. Decision Tree, Random Forest, XGBoost, Support Vector Machine, and a Multi-Layer Perceptron were trained on eight demographic and fiscal attributes, with a signed-logarithm transformation correcting skew in the tax-amount field; interpretability was assessed using Shapley Additive Explanations (SHAP). XGBoost achieved the best discrimination (AUC = 0.779, accuracy = 70.2%, F1 = 0.699), outperforming the other models compared. SHAP identified tax amount, tax category, and taxpayer age, a socio-demographic attribute, as dominant predictors, diverging from gain-based rankings. These findings indicate that duplicate contamination and non-grouped validation can distort administrative model evaluations, and that debtor-related attributes carry under-exploited predictive value for tax segmentation.

Abstract: Artificial intelligence (AI) is entering audit workflows while sustainability reporting and assurance expand the volume, variety and uncertainty of information subject to professional evaluation. The policy question is whether firms disclose governance arrangements that keep AI-assisted work human-led, reviewable and accountable. This exploratory study analyses the complete cross-section of the 2024 UK transparency reports of Deloitte, EY, KPMG and PwC, coded against seven pre-specified dimensions of AI–judgment governance and aggregated into a transparent, replicable AI–Judgment Governance Disclosure Index (AI-JGDI). The analysis is triangulated with the UK Financial Reporting Council's 2024 inspection results and interpreted against the ISAs, the IESBA Code, the EU Artificial Intelligence Act, the NIST AI Risk Management Framework, CSRD/ESRS, IFRS S1 and S2, and ISSA 5000. All four firms dis-close deployed AI capabilities and explicitly retain human professional responsibility; disclosure is strongest for human oversight, governance ownership and training, and least consistent for AI-specific validation and for explanations that would allow an external reader to reconstruct how an AI output affected an audit judgment. AI-JGDI scores range from 71.4 to 100.0. The study contributes a public-document method, a disclosure index and a governance framework for accountable AI-assisted judgment, and identifies limited explicit integration between AI governance and sustainability-assurance methodology.

Abstract: This conceptual article examines how artificial intelligence can support sustainability assurance in the transition from voluntary ESG disclosure to regulated, assurance-oriented sustainability reporting. The study is situated in the context of the Corporate Sustainability Reporting Directive, the European Sustainability Reporting Standards, ISSA 5000 and the EU Artificial Intelligence Act. It argues that AI can strengthen ESG verification by supporting disclosure identification, ESRS mapping, anomaly detection, consistency checks, greenwashing risk screening, external data triangulation and working-paper documentation. At the same time, AI introduces specific assurance risks, including data quality risk, reliability and hallucination risk, explainability risk, bias risk, overreliance risk, documentation risk, confidentiality risk, boundary and materiality risk, and accountability risk. The article develops a Responsible AI-Assisted Sustainability Assurance Framework that integrates ESG data inputs, AI analytical procedures, assurance risk assessment, human professional judgement, validation controls and documented assurance outputs. The central conclusion is that AI should be used as an analytical support layer within a human-in-the-loop assurance process, not as an autonomous source of assurance conclusions.