Accounting and Auditing

Dynamic panel data methods, and System GMM in particular, have become a methodological reference point in accounting research, yet their diagnostic performance has rarely been validated against the structural conditions characteristic of Southeast European (SEE) panels, namely SME dominance, short time horizons, and crisis-period volatility. This article applies and evaluates the System GMM estimator as a methodological benchmark for analysing dynamic accounting performance in Southeast European (SEE) panels. Firm-level performance indicators, most notably profitability and productivity, alongside export outcomes, exhibit precisely the econometric characteristics the Blundell–Bond estimator is designed to address persistence, bidirectional causality, and unobserved heterogeneity. Treated as performance-measurement constructs for management control and decision support, these measures are analysed in a firm-level panel of 284 Greek companies (ICAP DataPrisma, 2005–2017). Hansen and AR(2) diagnostics are not rejected across all specifications, establishing a reproducible methodological benchmark for accounting research under SEE conditions. The study further documents the estimator’s alignment with the standards of leading accounting journals, positioning it as an appropriate benchmark for analysing the dynamic evolution of accounting variables under the conditions examined. Full article

Sustainability reporting is moving from voluntary narrative disclosure toward regulated, evidence-based and externally assured corporate reporting, creating an assurance problem that artificial intelligence (AI) is expected to help address. Because AI is embedded in accounting and audit workflows, its outputs increasingly shape how assurance evidence is located, tested and evaluated. This conceptual article develops an assurance-specific framework answering three questions: for which sustainability-assurance procedures AI creates analytical value, which risks arise when AI influences assurance work, and which decision rights and controls should govern that influence. Integrating assurance standards, accounting and auditing research, AI-governance frameworks and behavioural studies, it finds AI adds value in five domains—evidence extraction, criteria mapping, anomaly and greenwashing screening, external-data triangulation, and documentation support—but only under defined base rates, error costs and source traceability. It identifies the risks limiting reliance: data, source fidelity, explainability, bias, calibration, preparer gaming, and auditor overreliance. The Responsible AI-Assisted Sustainability Assurance Framework sets graded reliance ceilings, non-delegable decisions, calibrated decision gates, anti-gaming safeguards and ex-post metrics, permitting clerical assistance, analytical recommendation and constrained agentic execution while prohibiting autonomous decisions on materiality, evidence sufficiency and conclusions. Illustrated in Europe, it generalises through ISSA 5000 as a testable model. Full article

This study examines whether digital skills demanded within the accounting function are associated with financial reporting quality. Using online job postings for listed firms from 2014 to 2022, I construct a firm-year proxy for accounting digital talent demand by identifying accounting vacancies that require digital skills. Firms with greater accounting digital talent demand exhibit lower accrual-based earnings management. The results hold after propensity score matching, entropy balancing, an instrumental-variable analysis, alternative earnings-management measures, and tests addressing the informativeness of job-posting text. Mechanism tests provide suggestive evidence consistent with improved cash-flow planning and investment efficiency. Cross-sectional analyses show stronger associations among firms with higher internal control quality and stronger external audit monitoring. The evidence links function-specific digital human capital to the financial reporting process. Full article

The paper aims to investigate whether regional innovation subsidies have a relevant effect for beneficiary firms, taking into account a couple of different regions in Italy, namely Lombardy and Sicily, characterized by different subsidy rules and local productive structures. The financial statements of beneficiary companies—analysed as two separate regional samples, 60 firms in Lombardy and 69 in Sicily—before the innovation program (2010) and after its fulfilment (2018) were compared and related to the obtained grant, separately within each region, in order to verify if a significant relationship exists. The two regional samples are analysed separately. The analysis includes beneficiary firms and contains neither rejected applicants nor non-beneficiary firms. Actually, in Lombardy, higher subsidy intensity is positively associated with revenue growth, personnel-cost growth, and intangible-asset growth, with the latter concentrated in manufacturing; in Sicily, no linear pattern emerges within the common intensity range. The main reasons of this contrast are discussed in the paper and have to be mainly associated with the different productive structures in the two regions and with the rules and procedures of the subsidy programs. Full article

Artificial intelligence (AI) tools—including audit data analytics, robotic process automation, machine-learning models, and generative AI—are changing how audit teams identify risks, select procedures, and evaluate evidence. At the same time, Regulation (EU) 2024/1689 (the EU AI Act) establishes a risk-based governance architecture built around risk management, data governance, technical documentation, logging, transparency, human oversight, robustness, cybersecurity, and post-market monitoring. The Act is not an auditing standard and does not directly regulate every tool used by audit firms. Nevertheless, its governance logic is relevant where audit firms develop, procure, or rely on AI-enabled systems that process sensitive client data, influence professional judgement, or become part of audit-relevant client systems. This conceptual study uses doctrinal requirements-to-controls mapping and design-oriented analysis to translate selected AI Act governance objectives into firm-level and engagement-level quality-management controls and into criteria for evaluating AI-enabled audit evidence. The paper specifies three modes of AI Act relevance: direct legal relevance where a regulated AI Act role is engaged; indirect relevance where AI compliance documentation becomes audit-relevant information; and benchmark relevance where the Act supplies governance objectives for quality management without creating an audit-law duty. The resulting artefacts are a traceable AI Act/IAASB standards crosswalk, an evidence-risk typology, a quality-management integration model, a documentation and review checklist, and a proportional maturity model. The framework clarifies when AI outputs remain triage or risk-assessment tools, when they provide directional or corroborative evidence, and the narrower conditions under which they may contribute to substantive evidence. It links reliance to data completeness, reconciliation, versioning, validation, false-positive and false-negative behaviour, explainability, logging, source-document corroboration, and reviewer challenge. The contribution is a scalable governance-to-controls framework that supports defensible reliance and inspection readiness without overstating the AI Act’s direct legal applicability. Empirical validation in audit firms remains a priority for future research. It further explains how quantitative risk features and anomaly-detection outputs feed into qualitative audit judgement: models can route attention to unusual transactions or documents, but evidential weight still depends on base-rate-aware error analysis, source-document corroboration, and reviewer challenge. Full article

Artificial intelligence (AI) is transforming the accounting function, but evidence from emerging economies still leaves a central question unresolved: whether the profession’s traditional strengths are sufficient to support this technological transition. This study examines this issue in Peru, where digital reform has advanced rapidly, but professional adaptation remains uneven. Using survey data from 424 accounting professionals and academics, a Covariance-Based Structural Equation Modeling (CB-SEM) approach was employed to test the relationships between Sustainability, Professional Challenges, IFAC Ethics, and AI Orientation. The construct previously described as AI adaptation is now defined more cautiously as AI Orientation, because its indicators capture a broad perceptual orientation toward AI, including perceived challenge, workflow impact, curricular integration, substitution concerns, and routine use, rather than verified adoption behavior. The results confirmed the strength of the traditional professional sequence: Sustainability significantly predicted Professional Challenges (β = 0.637; p < 0.001), and Professional Challenges strongly predicted adherence to IFAC Ethics (β = 0.972; p < 0.001). However, the direct path from Sustainability to IFAC Ethics was not significant (β = 0.200; p = 0.096). Regarding technological integration, the model revealed a structural disconnect. While Sustainability showed a weak direct effect on AI Orientation (β = 0.279; p = 0.045), neither Professional Challenges (β = 0.160; p = 0.510) nor IFAC Ethics (β = 0.037; p = 0.810) significantly predicted AI Orientation. These findings indicate that while the ethical and adaptive core of the Peruvian accounting profession remains highly resilient under pressure, its connection to AI is still fragmented, with algorithmic tools often perceived as external technological systems rather than integrated accounting competencies. Full article

We examine the association between Environmental, Social, and Governance (ESG) initiatives and corporate performance using a sample of 360 S&P 500 firms from 2010 to 2018. Employing MSCI ESG ratings and controlling for industry and time effects, we find that environmental initiatives positively associate with current profitability (ROA), while gender diversity correlates with long-term growth prospects (Tobin’s Q). This study moves beyond aggregated ESG metrics by providing a disaggregated analysis, revealing that different ESG dimensions affect performance through distinct financial mechanisms. To address common endogeneity concerns, we implement a rigorous empirical identification strategy, including propensity score matching, Heckman selection models, and instrumental variable approaches using industry-average instruments. Our results quantify the economic magnitude of these effects, demonstrating that a one-standard-deviation increase in environmental performance corresponds to a 0.92 percentage point increase in ROA, representing approximately $176 million in additional annual net income for the median firm. These findings provide theoretical advancement for the resource-based view and stakeholder theory by showing that specific ESG capabilities serve as valuable, inimitable resources. Ultimately, the study contributes standardized, high-resolution evidence on how specific ESG dimensions drive superior corporate performance. Through mechanism analysis, we show that environmental effects operate primarily via operational cost reduction and risk mitigation, while gender diversity creates value through enhanced innovation findings, which has direct implications for corporate ESG strategy. Full article

Enterprise Resource Planning (ERP) systems have long served as the primary infrastructure for internal control in financial governance, functioning as deterministic, auditable systems of record. The emergence of Zero-Trust Architecture (ZTA) and artificial intelligence (AI) progressively challenges this model, transforming enterprise systems from passive ledgers into autonomous systems of judgment capable of influencing decisions with direct financial and regulatory consequences. This study investigates how trust mediates this transformation. Drawing on a longitudinal dataset of 968 survey responses collected across five measurement waves during a ZTA deployment in a multinational telecommunications organization, we apply an extended Technology Acceptance Model (TAM) to examine changes in perceived usefulness, ease of use, and trust. The findings reveal an Audit Paradox: ZTA simultaneously strengthens formal compliance controls while eroding user trust and perceived productivity, with only partial recovery following structured governance interventions. Building on these findings, we introduce a trust-contingent framework for ERP evolution and develop the concept of the Agency Gap, a structural misalignment between algorithmic decision-making authority and institutional accountability. This study extends accounting and auditing theory into AI-driven control environments and offers practical guidance for auditors, CFOs, and technology leaders navigating the governance of increasingly autonomous digital systems. This study contributes by empirically demonstrating the trust-mediated dynamics of advanced control architectures, introducing the Agency Gap as a theoretical construct addressing algorithmic accountability in AI-driven governance, and extending Sarbanes–Oxley (SOX) oriented control theory into probabilistic, algorithmic environments. Full article

Service provision often requires financing that does not coincide with the normal transaction activity of the current fiscal year. The deferred outflow is a method that allows for both policy implementation and funding transparency. No previous research exists concerning the practice of deferred outflow utilization and the impact of these resources on future funding activities. This study examines the levels of deferred outflows among county governments in North Carolina. Preliminary findings indicate a very high level of outflows dedicated to pension-related programs for employees who provide services in traditional government activities. A logistic regression analysis suggests the total number of employees, an overall increase in net position from the previous year, and the presence of more personnel who perform accounts payable activities were all significantly related to elevated levels of deferred outflows. Findings also suggest that smaller governments were more likely to increase deferred outflow resources at a higher rate on an annual basis. Implications of the study include the importance of proper budgeting and fund accounting for employee post-service and the need for experienced finance personnel staff that can adequately implement transactions and compile comprehensive audits. Full article

While artificial intelligence offers significant efficiency gains, accounting professionals evaluate adoption readiness through the specific lens of task-level accountability. This study draws on structured task-perception ratings from 24 practitioners across 24 accounting tasks (576 task-level observations) to examine task-level attributes associated with adoption readiness. The analysis finds that automation potential is positively associated with readiness, suggesting professionals are more open to AI assistance on tasks with higher substitutability. Integration feasibility, defined as the ease with which technology can be embedded into existing workflows, similarly shows a positive association, indicating that governance fit facilitates rather than hinders adoption. Familiarity with large language models shows a negative association, consistent with an experience-skepticism effect whereby greater exposure heightens awareness of technical limitations. The necessity for human judgment is negatively associated but does not reach conventional significance thresholds, suggesting its role may be contingent on the specific nature of the task. Supplementary cluster analysis identifies three distinct task profiles, ranging from judgment-first to automation-oriented, that offer a practical prioritization framework for AI deployment sequencing. Collectively, the findings indicate that task-governance fit, rather than efficiency incentives alone, is associated with adoption readiness among accounting professionals. These results are theory-building and intended to generate hypotheses for larger confirmatory studies. Full article

Digital tax reforms are implemented through staged, publicly announced milestones, yet policymakers rarely have timely indicators of whether these signals mobilize information-seeking and whether such demand can be anticipated for operational planning. We analyze monthly Google Trends series for Greece’s myDATA/e-invoicing rollout (2016–present) using preregistered event study models that separate step changes from post-event trend shifts with HAC-robust inference, and we evaluate 1–3-month predictive performance via rolling-origin cross-validation against a seasonal-naïve benchmark. Search-based attention shifts appeared most clearly in application-related queries: invoicing app terms spike around visible rollout phases (≈+34 to +38 index points over six months) and decline around VAT–myDATA alignment (≈−34 to −43). Ecosystem attention (the “Electronic invoicing” topic) exhibits large, opposite-signed movements (≈−53 around public-sector expansion; ≈+46 around VAT alignment), whereas platform terms show smaller and less regular responses; a back-office milestone produces no detectable change. In out-of-sample tests, event-aware regressions improve short-horizon accuracy for platform terms (≈40–50% MAE reduction at one month; ≈18–32% at two to three months), with series- and horizon-dependent results elsewhere. Overall, the evidence supports using search activity as an intermediate planning signal—informative about when and where guidance demand concentrates but not evidence of compliance. Full article

This article addresses a major challenge in circular economy accounting: assessing the social dimension, particularly social ties, which are often immaterial and difficult to capture. It examines a case study of how a local project managing organic waste and unsold goods fosters social ties in a priority urban neighborhood in France, and how these dynamics can be apprehended through an alternative qualitative accounting approach. The study draws on an ethnographic case of the MatOrGa project, combining participant observation, semi-structured interviews, discourse grounded analysis, and actor and flow mapping. Situated within counter-accounting and critical accounting, the research emphasizes social ties that extend beyond purely economic logic, spanning social, ecological, and economic dimensions. The new concept of counter-accounting utterances is introduced to describe empirical accounts that make visible practices, relationships, and social effects often overlooked in conventional accounting and sustainability reporting. The study shows how ethnography can function as a form of counter-accounting, producing qualitative representations of social impact that resist standardization. The findings advance social and sustainability accounting by offering a situated and reflexive approach to assessing the social impact of circular economy initiatives, while also opening the way for context-sensitive non-financial reporting. Full article

Background: This paper is a systematic review of 100 peer-reviewed articles (2015–2025) related to artificial intelligence (AI) applications in the auditing field, and includes machine learning, natural language processing, robotic process automation, and other AI methods. Purpose: The paper delves into the integration of these AI technologies into the audit workflow; empirical implications of these technologies on audit effectiveness; efficiency and quality; and technical, organizational, and regulatory obstacles that suggest more widespread adoption is still limited. Methods: Five large-scale databases and other sources were searched and selected using PRISMA; structured data were extracted, assessed in quality and narrative, and thematically analyzed. Results: The discussion indicates that machine learning-based anomaly detection and predictive analytics, document analysis through NLP, and automation through RPA are becoming part of planning, risk assessments, control tests, and substantive procedures/reporting, with improvements in detection capabilities, coverage and efficiency reported in various empirical and design science studies. The review also presents common architectural models of AI-enabled audit processes, including layered data and governance, model development and oversight, orchestration and automation, auditor-facing applications, and human-in-the-loop controls. Conclusions: The article proposes an AI-based audit workflow reference architecture and summarizes evidence on opportunities, threats, and implementation obstacles, highlighting gaps in longitudinal assessment, comparative evaluation of AI methods, and regulatory recommendations. The results have practical implications for auditors, standard-setters, and system designers seeking to revise the audit approach and regulations to enable AI-driven assurance. Full article

This study provides a critical examination of Nike’s sustainability reporting by comparing disclosures across six major frameworks: the Higg Index, the Global Reporting Initiative (GRI), the Sustainability Accounting Standards Board (SASB), the Sustainable Development Goals (SDGs), the Triple Bottom Line (TBL), and Double Materiality. Drawing on a directed content analysis of Nike’s 2022–2023 sustainability documents, the research codes and compares how these frameworks are applied to environmental, social, and governance topics. The analysis shows that Nike’s environmental reporting is the most consistent and well-developed across the six frameworks. In contrast, significant gaps and inconsistencies remain in areas such as labor rights, living wages, and supply chain transparency. These findings reveal both the advantages and the tensions that come with using multiple frameworks, illustrating where they reinforce one another and where they diverge. Overall, the study highlights the essential need for harmonized reporting practices across the global apparel sector. It also reflects both the strengths and the limitations of using multiple frameworks to produce sustainability reports that are transparent and comprehensive. Full article

Background: The increasing integration of Environmental, Social, and Governance (ESG) factors into financial decision-making has prompted debate over their impact on corporate credit risk. While many studies suggest that ESG performance may enhance firms’ resilience, empirical evidence remains mixed due to data inconsistency and methodological heterogeneity and differences in time horizons over which ESG effects materialise. Methods: The study investigates the relationship between ESG performance and credit risk using a panel of European firms from 2020 to 2024, a phase highly characterised by substantial macroeconomic shocks. The Altman Z-score serves as a proxy for default risk, while ESG data are sourced from Refinitiv Eikon. Four fixed-effects panel regressions are estimated: a baseline model using aggregate ESG scores, an extended model with financial controls, and disaggregated and sector-specific models. Results: The findings indicate that ESG scores—either aggregated or by pillar—show limited statistical significance in explaining variations in the Z-score. In contrast, financial variables such as solvency, liquidity, and cash flow ratios display strong, positive, and significant effects on credit stability. Some heterogeneous sectoral effects emerge: social factors are positive in technology, while governance has a negative impact in basic materials. Conclusions: ESG initiatives may not yield immediate improvements in default risk metrics, particularly over short and crisis-dominated periods, but could enhance financial resilience over time. Combining ESG information with traditional financial ratios remains essential; the results underscore the importance of consistent and high-quality ESG disclosure to reduce measurement error and enhance comparability across firms. Full article

The use of financial derivatives to hedge economic risks presents several operational and financial reporting challenges to corporations. Special hedge accounting treatment is stringent and complex; different accounting treatments may be used for similar instruments, and risk management strategies, expertise, and judgment are necessary in valuing certain instruments; and careful monitoring and internal controls processes and procedures are necessary to ensure that risks are properly hedged. This study examines whether the use and the extent of the use of financial derivatives are associated with audit risk, financial restatements, and internal control weaknesses. Using a sample of over 6000 firms across non-financial industries from 2012 to 2022, I find that derivative use is associated with an increase in audit fees, restatements, and internal control weaknesses. The fair value of total derivatives used is associated with an increase in audit fees and internal control weaknesses. These findings provide evidence on the hidden costs of derivatives; the auditor’s price increased audit risk in audit fees, and the additional resources needed to support derivative hedges expose firms to additional financial reporting and internal control risks. Full article

This study analyses disclosures on materiality in non-financial information (NFI) reporting by examining their likely explanatory factors, including entities’ financial or structural characteristics, governance features, and contextual factors, grounded in a set of relevant theories. Based on archival research and content analysis, this study uses consolidated NFI reports from 2021 of entities listed in the main Euronext indices. The descriptive analysis reveals that while 71% of companies present a materiality matrix, only about half (50%) meet all eight criteria of materiality disclosure, with double materiality being addressed by just 16%. The regression results show that the level of materiality disclosure is significantly and positively associated only with the size of the board of directors, whereas other expected relationships, such as those with firm size, profitability, or debt, were not statistically significant, challenging traditional assumptions from stakeholders, agency, and positive accounting theories. These findings suggest that governance structures may play a more decisive role in transparency regarding materiality than the entities’ financial or structural characteristics. This research contributes to both the academic literature and practice by identifying explanatory factors and empirical patterns in materiality disclosure in NFI reporting, which may be relevant for standard-setting bodies, regulators, auditors, and stakeholders. Full article

This study investigates the systemic interrelations between logistics performance, environmental performance, sustainable development progress, and institutional governance. While the existing literature often examines these dimensions separately, this research conceptualizes them as co-determined drivers of national development. Using data from 123 countries, the analysis integrates four composite indices—Logistics Performance Index (LPI), Environmental Performance Index (EPI), Sustainable Development Goals Index (SDG), and Worldwide Governance Indicators (WGI)—alongside GDP per capita. Methodologically, this study applies multiple linear regressions and correlation analyses to assess the associations among these variables and employs Fuzzy Cognitive Mapping (FCM) to simulate scenario-based systemic interactions. Results show that all ESG indicators are positively and significantly associated with LPI, with WGI exerting the strongest effect. In turn, LPI, EPI, SDG, and WGI jointly explain 81.7% of the variance in GDP per capita, confirming their integrated role in shaping economic performance. FCM simulations further reveal that both environmental and institutional improvements generate reinforcing effects on logistics capacity and GDP outcomes. This study’s originality lies in its multiple-method approach and its synthesis of ESG and logistics performance metrics into a unified explanatory framework. It contributes to development studies by highlighting the structural embeddedness of logistics within broader institutional and sustainability ecosystems. Its policy implication lies in suggesting that integrated reforms—combining infrastructure, regulatory quality, and environmental stewardship—are essential for enhancing long-term national competitiveness and resilience. Full article

This research examines investing decisions when the company under consideration has an auditor with other clients who have had financial statement restatements and regulatory enforcement actions. Another issue addressed is whether investors’ tolerance for ambiguity affects these investing decisions. Participants were given a questionnaire involving an investment decision and were asked to provide risk assessments and investment amounts. They were assigned to one of two treatment groups, each of which described the same hypothetical company scenario except for its audit firm’s associations with other clients. One group was informed that the company’s auditor has had other clients who have recently had financial statement restatements and regulatory enforcement actions. The other group was informed that the company’s auditor has not recently had any clients who have had financial statement restatements or regulatory enforcement actions. Participants’ tolerance for ambiguity was measured with a commonly used metric. Results indicate that knowledge about an audit firm’s associations with other clients did not significantly impact either investors’ risk assessments (5.76 vs. 6.17 on a scale from 1 to 10) or investment amounts ($4917 vs. $4227). This research also did not find evidence that participants’ tolerance for ambiguity influences risk assessments (6.20 vs. 5.72 on a scale from 1 to 10) or investment amounts ($4857 vs. $4309). Full article