Publications

Purpose This study examines how multilateral trade policy uncertainty (TPU), i.e. simultaneous tariff threats against several trading partners, affects US stock market volatility. Unlike prior work on bilateral disputes, it focuses on the compound uncertainty created by the April 2025 tariff announcements under former President Trump and on how responses vary across sectors with different global trade exposures. Design/methodology/approach Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) models are estimated on daily returns for the S&P 500 and three sectoral indices (technology XLK, industrials XLI, utilities XLU) from January to April 2025. Tariff-announcement dummies capture policy shocks, ANOVA tests compare volatility persistence across sectors and models are estimated by quasi-maximum likelihood with robust standard errors. Findings Tariff announcements raised volatility sharply: S&P 500 conditional volatility doubled from 0.8% to 1.6% within five days, with a 23-day half-life. Responses were heterogeneous, with persistence of 0.96 and 0.93 for technology and industrials versus 0.72 for utilities, and ANOVA rejected equality across sectors. EGARCH results show asymmetry: negative shocks raise volatility by 0.14%, while equivalent positive shocks reduce it by only 0.06%, consistent with the leverage effect and loss aversion. Practical implications During trade disputes, investors should rebalance toward domestic-oriented sectors, while policymakers should favor gradual and clear communication of trade measures to limit adverse market effects, especially in globally integrated sectors. Originality/value The paper offers early evidence on multilateral TPU and a framework for its transmission, showing that simultaneous, multipartner tariff threats generate compound uncertainty and volatility persistence beyond those documented for bilateral trade disputes.

This study examines how customers' perceptions of green banking practices shape their advocacy behaviour towards their banks, and whether environmental concern mediates and corporate image moderates this relationship. Drawing on signaling theory, the study frames green banking initiatives as credible signals that influence customer attitudes and behaviour. Survey data were collected from 705 bank customers in Jordan and analysed using partial least squares structural equation modelling with 5,000 bootstrap samples. The results show that perceived green banking practices significantly increase customer advocacy, both directly and indirectly through heightened environmental concern, which emerges as the strongest driver of advocacy. Corporate image strengthens the link between environmental concern and advocacy but weakens the direct effect of green banking practices on advocacy, suggesting diminishing returns for high-prestige banks whose green signals are treated as expected rather than distinctive. The findings extend signaling theory to the green banking context and offer guidance for managers seeking to convert sustainability initiatives into customer advocacy, sustainable growth, and competitive advantage in the financial sector.

• corporate image
• customer advocacy
• environmental concern
• green banking practices
• Jordanian banking sector
• PLS-SEM
• Issue3 515

This study examines the association between environmental, social and governance (ESG) controversies and the subsequent financial performance of firms listed in the United States and whether the information environment of the firm conditions this association. Most of the literature measures the sustainability conduct of the firm through its own disclosure and the ratings built from that disclosure, while negative events recorded by external media sources have received less attention. Using a panel of 14,210 firm-year observations for 1737 NASDAQ and NYSE firms over 2014 to 2025 from the LSEG Refinitiv database and a two-way fixed effects model with standard errors clustered by firm, the study finds that a firm that recorded an ESG controversy in the previous year reports a return on assets lower by about 0.50 percentage points and a lower Tobin’s Q relative to the same firm in years without a controversy. The return on assets result is the more consistent of the two across the alternative specifications, while the Tobin’s Q result is supportive but more sensitive to the specification and to the composition of the sample. The pre-event lead coefficient is not significant at the 5 percent level for the return on assets, and analyst coverage does not respond to a controversy, which speaks against a simple reverse-causality reading. The evidence is consistent with heterogeneity in the association with the return on assets, which is concentrated among firms followed by few financial analysts and is not detected among firms in the highest tercile of analyst coverage, whereas no such heterogeneity is found for Tobin’s Q. The results are robust to sector-by-year fixed effects, to the exclusion of the pandemic years, to lagged controls, and to alternative treatment definitions, and they are reported in full together with the specifications in which the estimates lose precision. The principal limitation is that a fixed effects design with observational data documents within-firm associations and cannot by itself establish causal effects.

• ESG practices
• ESG controversies
• sustainable governance
• external monitoring
• corporate financial performance
• analyst coverage
• corporate sustainability
• United States

This study examines the mediating role of investor trust in the relationship between perceived blockchain integration and perceived stock market efficiency within the Amman Stock Exchange (ASE). The Amman Stock Exchange (ASE), established in 1999, is the sole securities exchange in Jordan and one of the leading emerging markets in the Middle East and North Africa (MENA) region. Drawing on technology acceptance theory, trust theory, and market efficiency theory, the research develops and tests a dual-pathway model wherein perceived blockchain integration relates to perceived market efficiency both directly and indirectly through investor trust. Using structural equation modeling with data collected from 400 market participants, the findings reveal that perceived blockchain integration is significantly and positively associated with investor trust (β = 0.849, p < 0.001) and with perceived stock market efficiency (β = 0.448, p < 0.001). Importantly, investor trust partially mediates this relationship (β = 0.380, p < 0.001), confirming the dual-pathway impact. Among blockchain dimensions, security demonstrates the strongest effect on both investor trust and market efficiency. The study contributes to the emerging literature on blockchain in financial markets by empirically validating the psychological mechanisms through which technological innovations translate into more favorable perceptions of market functioning. For market regulators and exchange administrators, the findings suggest that comprehensive blockchain implementation strategies should address both technological deployment and trust-building initiatives to strengthen favorable investor perceptions of market efficiency in emerging markets.

Digitization is reshaping how banks operate, yet whether it improves corporate financial performance remains unsettled. This study examines how the digital transformation that banks disclose relates to the structure of their key financial performance indicators. Using a balanced panel of 73 listed Gulf Cooperation Council (GCC) banks over 2020–2025 (438 bank–year observations), digital transformation is measured by a text-mined digital disclosure index (DDI, 0–100) constructed from annual reports and decomposed into nine themes. Bank fixed-effects regressions with Driscoll–Kraay standard errors, one-year-lagged specifications, and two-step system GMM are estimated across profitability, net interest margin, cost efficiency, credit risk and capital adequacy. Disclosed digitization more than doubled over the window, but its associations with performance are conditional rather than uniformly positive. Within banks, a higher DDI value is associated with wider net interest margins, yet also with lower profitability, higher cost-to-income ratios, modestly higher credit risk, and thinner capital buffers. This pattern is consistent with an investment or build-out phase in which the costs of digital transformation are visible before any efficiency or stability dividend and in which margins are the single offsetting benefit. The six-year window observes only this cost-bearing segment and not any later recovery, so the study documents the investment-phase drag rather than a completed cycle. The theme decomposition indicates that the margin association is closest to regulatory technology, cybersecurity, broad transformation and payments. Because the design is observational, the results are interpreted as within-bank associations rather than causal effects, and, although precisely estimated, these associations are economically modest. The study contributes a transparent theme-decomposed measure of bank digitization and evidence on its limits for corporate financial performance in an emerging-market banking region.

• digitization
• corporate financial performance
• financial management
• cost efficiency
• capital adequacy
• credit risk
• banking
• GCC

Environmental, social, and governance (ESG) ratings are now central to how firms’ sustainability is assessed, yet leading providers disagree sharply, injecting measurement error and uncertainty into any rating-based decision. This study proposes and applies a transparent, fully reproducible alternative for the environmental pillar: an ordinal, sector-based carbon-exposure measure constructed solely from firms’ publicly observable industry classifications, free of vendor scoring choices and reconstructable at zero cost. The measure captures structural, between-sector carbon exposure; it is not a firm-level environmental-performance score and does not use reported emissions. Using 3067 firm-year observations on 1049 listed non-financial firms that carry an observed sector classification across six developed markets (the United States, the United Kingdom, Australia, France, Italy, and Spain) over fiscal 2021–2025 (with a small number of early fiscal-2026 period-ends), we assess the measure’s informativeness by estimating its association with accounting profitability in high-dimensional fixed-effects models. Descriptively, more carbon-intensive sectors are less profitable (a monotone brown tilt), but this association is not robust: once firm controls and country-by-year fixed effects are absorbed, carbon exposure is not significantly related to return on assets, return on equity, or net margin, and there is no evidence of a concave (inverted-U) pattern. The measure does recover a real operating difference, higher asset turnover among carbon-intensive sectors, but this is offset by margins and does not translate into a profitability premium. A within-firm design detects no differential effect of mandatory non-financial reporting. The contribution is a transparent, rule-based environmental measure that sidesteps rating divergence; the honestly reported null on profitability cautions that apparent effects are sensitive to sample construction and sectoral composition, and positions the measure as a complement to, rather than a substitute for, proprietary ESG ratings.

• ESG performance assessment
• construction of ESG measures
• ESG rating divergence
• environmental exposure
• carbon intensity
• firm profitability
• transparent measurement
• legal origin

This study examines how sustainable business models (SBM) shape the quality of environmental, social and governance (ESG) reporting, and whether technology adoption and triple bottom line (TBL) performance sequentially mediate this relationship. Drawing on the natural resource-based view, stakeholder theory and the technology–organization–environment framework, survey data from 350 auditors, accountants and ESG managers across six industry sectors were analysed using partial least squares structural equation modelling to test nine hypotheses. SBM exerts strong direct effects on ESG reporting quality, technology adoption and TBL performance. The serial path running from SBM through technology adoption and TBL performance to ESG reporting quality is significant, whereas the direct effect of technology adoption on reporting quality is not, indicating that technology improves disclosure quality indirectly through realised sustainability performance rather than directly. The study provides the first empirical test of a serial mediation model linking SBM to ESG reporting quality through technology adoption and TBL performance. The findings imply that firms should embed sustainability within their business model architecture and align technology investment with sustainability performance rather than treating technology as a stand-alone reporting solution.

• ESG Reporting Quality
• PLS-SEM
• Serial Mediation
• Stakeholder Theory
• Sustainable Business Models
• Technology Adoption
• Triple Bottom Line

Does mandatory sustainability disclosure improve the quality of corporate financial reporting immediately, gradually, or with delay? We address this question using Saudi Arabia’s January 2021 Tadawul ESG Disclosure Guidelines—the first comprehensive sustainability disclosure framework in the Gulf Cooperation Council and a uniform, accurately dated regulatory shock affecting all listed firms. Using a balanced panel of 135 non-financial firms over 2017–2024 (1080 firm-year observations), we estimate absolute discretionary accruals from the Modified Jones Model and employ event-time fixed-effects regressions with Driscoll–Kraay standard errors robust to heteroskedasticity, autocorrelation, and cross-sectional dependence. We document a temporal paradox: reporting quality did not change in the announcement year (2021), deteriorated significantly in 2022 (+28%) and 2023 (+38%) relative to the pre-reform baseline, and then improved significantly in 2024 (−17%). The pattern survives performance-matched discretionary accruals, exclusion of the 2020 COVID-19 year, a placebo test, sectoral disaggregation across nine Tadawul-aligned industry groups, and a battery of pre-reform firm characteristics. Heterogeneity analysis identifies the underlying mechanism: voluntary pre-2021 ESG disclosers and firms with stronger pre-reform governance exhibit amplified short-run deterioration, while larger firms with pre-existing reporting infrastructure show a substantially attenuated paradox. These patterns are jointly consistent with the adjustment-cost mechanism we develop: the reform redirected scarce reporting governance toward the new disclosure margin during a three-year compliance buildout, after which the constraining effect on accrual-based earnings management emerged. The findings carry direct implications for the design and evaluation of mandatory sustainability disclosure reforms currently advancing across emerging and developed markets.

• sustainability disclosure
• earnings management
• reform paradox
• Saudi Arabia
• Tadawul
• corporate reporting quality
• ESG
• event-time analysis
• emerging markets Sustainability 2026
• 6582 https://doi.org/10.3390/su18136582 Sustainability 2026

This paper explores how green digital technologies (GDTs)—ERP systems, cloud software, IoT, artificial intelligence, and big data analytics—can be used to improve the quality of ESG disclosures of industrial listed companies in the Amman Stock Exchange (ASE). Based on the institutional isomorphism theory, we examine how the relationship between coercive, mimetic, and normative institutional pressures and adopting green technology interacts to effect sustainability reporting practices. Using panel data pertaining to 30 ASE-listed industrial companies during the 2020–2024 period (N = 146 firm-year observations), we applied pooled OLS and random effects frameworks characterized by a strong clustering of standard errors. The findings show that the Green Digital Technology Index is positively and significantly associated with ESG disclosure scores (Pooled OLS: β = 5.448, t = 2.367, p = 0.019; Random Effects: β = 5.941, t = 2.507, p = 0.024), with adopting firms having an average score that is 1.73 points higher. Its largest effect is on the environmental dimension (β = 3.460, p = 0.074). Institutional pressures do not moderate the GDT–disclosure relationship; however, mediation analysis indicated that institutional pressure significantly predicts GDT adoption (β = 0.098, p < 0.001), suggesting that institutional forces are linked to disclosure quality through their association with technology adoption rather than through direct effects, indicating that institutional forces exert their influence through technology adoption. Disclosure quality is negatively associated with CEO duality (β = −4.863, p < 0.001). These results are consistent with the interpretation that green digital technologies serve as a transmission channel through which institutional pressures are associated with enhanced sustainability disclosure in emerging markets.

• green digital technologies
• sustainability disclosure
• IFRS S2
• institutional isomorphism
• emerging capital markets
• circular economy
• Amman Stock Exchange
• ESG reporting
• net-zero transition
• sustainable business transformation

Purpose - This study combines traditional econometric methods with machine learning (ML) algorithms for predicting firm performance among Jordanian listed firms and ownership structure dimension (institutional, foreign, governmental and ownership concentration) and Board diversity are considered as a predictor and augmented with artificial intelligence driven analytical features Design/methodology/approach - In this study, Panel data of 158 firms listed on the Amman Stock Exchange (ASE) for the period 2015 - 2021 (1,106 firm-year observations) is utilized as the base data set. This is extended to 2024 based on 1,580 observations combination (Monte Carlo simulation) calibrated from historical three variables distribution parameters. The study takes a two-stage analytical framework: (1) system Generalized Method of Moments (GMM) to determine causality and moderation analysis and (2) Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Artificial Neural Networks (ANN) to forecast Return on Assets (ROA) and Tobin's Q (TQ). Findings - GMM results confirm the positive impact of ownership concentration, foreign ownership and government ownership on ROA and positive impact of ownership concentration, foreign ownership and institutional ownership of TQ. Board diversity has a positive moderating effect on the foreign ownership - performance nexus in terms of both measures. ML models have better predictive accuracy (R2 = 0.81-0.89) than the benchmarks of OLS (R2 = 0.23-0.31). XGBoost has the best out-of-sample accuracy, from SHAP analysis, we can see that ownership concentration and firm size are most important in predicting. Originality/value - This is one of the first studies to use a combination of system GMM and ML ensemble methods to examine the relationships between corporate governance and performance in a country in the Middle East emerging market, which offers a new hybrid methodology for studying corporate governance.

• Machine learning
• Firm performance
• Ownership structure
• Board diversity
• GMM
• Random Forest
• XGBoost
• Neural networks
• Jordan
• Amman Stock Exchange

This paper investigates how the accounting technologies that are driven by artificial intelligence (AI) affect the financial performance of the financial companies that are listed in the Amman Stock Exchange (ASE) in the period of 2022-2025. With a panel sample of 96 financial companies, we examine the effects of AI use in accounting on the primary performance indicators such as return on assets (ROA), return on equity (ROE), operational efficiency, and quality in earnings. We find that AI-based accounting systems have a tremendous positive effect on financial performance, and adopters show an average ROA difference of 3.2% and ROE of 4.7% relative to non-adopters. The latter impacts are favorable to bigger organizations and those that are more prepared in digital infrastructure. We also record the changes in quality of audit, minimal accounting errors, and increased regulatory compliance among AI adopters. The research also adds to a growing body of research on the topic of digital transformation in accounting by not only providing empirical evidence on the topic of the understudied Middle East market but also has implications on how financial institutions should deal with technological disruption in emerging economies.

• Artificial Intelligence
• Accounting Automation
• Financial Performance
• Jordan
• (2026)

The fast adoption of artificial intelligence (AI) in the accounting profession has created a lot of fear among professionals on the issue of occupational displacement and professional relevance. The present study analyzes the connection between the perceived threat of AI replacement and job performance in certified accountants and technology anxiety being the mediating variable. Perceived AI replacement threat is conceptualised to be used on a second order formative construct based on Conservation of Resources (COR) Theory and Technology Threat Avoidance Theory (TTAT) and includes four first order reflective dimensions including: job security threat, career threat, professional status threat, and competence threat. A cross-sectional survey of 401 certified and practising accountants working in Jordan was carried out through quantitative measurement and the data analysed through Partial Least Squares Structural Equation Modelling (PLS-SEM) in SmartPLS 4. The findings show that there is a strong negative direct impact on job performance ( = -0.598, p < 0.001) and a strong positive impact on technology anxiety ( = 0.753, p < 0.001) created by perceived AI replacement threat and in the process significantly negatively affects performance. The mediation analysis confirms that technology anxiety plays a significant mediating role between the threat and performance relationship with competence threat and job security having the greatest indirect effect using the anxiety pathway. The model has good explanatory strength with 56.7 and 55.9 percent is explained by the model on technology anxiety and job performance respectively. The current study will add to the emerging body of research on AI-human dynamics within the framework of professional services by not only presenting a multidimensional threat framework, but also empirical data on the subject in a developing-economy setting, and giving results applicable to both the practice of accounting firms, profession-related professional bodies, and policymakers in order to control the psychological effects of AI-driven change on practitioner performance.

• Artificial Intelligence
• AI Replacement Threat
• Technology Anxiety
• Job Performance
• Accountants
• Jordan
• (2026)

This paper investigates how the three pillars of environmental, social, and governance (ESG) disclosure differentially shape accrual-based earnings management (AEM) in Saudi Arabia's Vision 2030 era, and how firm profitability moderates these relationships. Using a balanced panel of 135 non-financial firms listed on the Saudi Stock Exchange (Tadawul) over 2016--2024 (1,215 firm-year observations), we estimate hierarchical fixed-effects regressions with Driscoll--Kraay standard errors. We document three findings that challenge the conventional aggregate ESG-AEM literature. First, governance disclosure operates as a substantive constraint on earnings management, while environmental disclosure operates as a camouflage mechanism that increases discretionary accruals; social disclosure shows no detectable effect, consistent with regulatory saturation. Second, profitability fundamentally reshapes these relationships: among unprofitable firms, environmental disclosure facilitates camouflage, but among profitable firms, the same disclosure transitions into a constraining mechanism---a sign reversal that occurs around a return-on-assets threshold of approximately five percent. Third, the governance constraining effect attenuates significantly following the October 2021 Tadawul ESG Disclosure Guidelines, suggesting that mandatory disclosure dilutes the informational value that previously distinguished voluntarily transparent firms. These findings cannot be detected when ESG is treated as an aggregate construct, and they reveal that the constraining-versus-camouflage debate that has long divided the disclosure literature is not a question of which mechanism prevails on average, but of which pillar is observed and under what firm-level conditions. The results carry implications for the design of disclosure regulation in emerging markets, where institutional reform agendas such as Vision 2030 increasingly shape corporate reporting practice. JEL classification: M41, G34, Q56, M48.

The paper presents the largest empirical evaluation of the adoption of FinTech and its impact on bank performance in the Gulf Cooperation Council (GCC). Over the 2020–2025 period, we form a balanced sample of 73 Saudi Arabian, United Arab Emirates, Kuwait, Qatar, Bahrain and Oman listed commercial and Islamic banks, which give us 438 bank-year observations. Bank-level financial indicators (ROA, ROE, NIM, Cost-to-Income, NPL, LLP and Capital Adequacy) are based on audited annual reports and Pillar III disclosures; observational gaps are bridged through linear interpolation based on country-year sector medians in the KPMG GCC Listed Banks Results Reports. The FinTech indicators at country level are based on the World Bank Global Findex, the ITU DataHub, MAGNiTT venture-capital databases and central-bank regulatory-sandbox registers. An overall FinTech Adoption Index is built and experimented with its constituent parts. The effects of FinTech on bank profitability, efficiency, risk and capitalisation are estimated using two-way fixed-effects panel regressions with Driscoll–Kraay standard errors, and system-GMM robustness tests. We theorise that FinTech adoption increases profitability and efficiency but narrows net-interest margins, with non-uniform impacts across Islamic and conventional banks and across regulatory regimes. Findings present evidence-based policy advice to policymakers who realise Vision 2030/2035 digital-finance aspirations. This article adds to the work of Kayed et al. (2026), who implemented a qualitative scenario-based MCDA framework to Kuwait, with a quantitative cross-country complement based on observable bank-level performance.

This research applies the principles of human resource accounting (HRA) and intangible asset valuation frameworks under IAS 38/IFRS to examine the relationship between the quality of player performance metrics, human capital metrics, and the quality of their financial reporting on the market valuation of football players and the financial performance of the leading football clubs in Europe. Based on a dual-level database, composed by 20 leading European clubs (club-level) and by 120 players (player-level) in the season 2023/24, the study constructs a performance-adjusted valuation model for estimating the interconnection between on-field statistics (goals, assists, expected goals, defensive actions, and performance indices imposed on a composite measure) and accounting or financial number (transfer fees, amortization charges, intangible asset values, book values) and financial results (ROA, club market valuation). The Outcome of multiple OLS Regression Models using Robust Standard Errors shows that Performance Index is the most important predictor of player market value (max 0.497, p &lt; 0.01) whereas club revenue is the most important predictor of club market valuation (max 0.009, p &lt; 0.01, R2 = 0.879). The market to book ratio analysis shows systematic difference between economic value and accounting book value based on player age, duration of contract signed, and performance indicators (Adj. R² = 0.363). The Moderated regression shows existence of positive moderating relationship between IFRS compliance and Big4 audit quality with on-field performance and financial outcomes. The findings add to the intersection of sports finance, accounting and human capital theory, stressing the inadequacy of current IAS 38 provisions in capturing the true economic value of football players as human capital assets.

Background: The advent of agentic artificial intelligence (AI) is a paradigm shift in auditing practices, from the traditional rule-based automation towards autonomous, goal-oriented machines that have the capacity to make decisions without human interaction. Agentic AI systems use large language models (LLMs) as well as multiple agents to perform complex audit procedures with very little human interaction. Objective: Within a literature review in a systematic way, an analysis of the current situation for the application of agentic AI in auditing will be accomplished, the relevant frameworks and architectures, advantages and challenges will be assessed and some recommendations for research and practice will be introduced. Methods: Using the criteria recommended by PRISMA 2020, we performed a systematic search of academic databases (Web of Science, Scopus, IEEE Xplore and ACM Digital Library) and grey literature from the period January 2020 to February 2026. Studies were evaluated using formative inclusion and exclusion rules and finally 47 studies were included in a synthesis. Results: The applications uncovered during the review were among five major areas: (1) financial statement audit (2) internal audit and control testing (3) compliance and regulatory audit (4) fraud detection and risk assessment (5) audit planning and resource assignment. The most important agentic AI frameworks include CrewAI, LangGraph, AutoGen, and audit-focused systems. Major benefits are increased efficiency (40-60% reduction of time), increased accuracy (15-25% reduction of error), continuous monitoring capability and scalability. Critical challenges include limitations of transparency and explainability, regulatory uncertainty, data security, data ownership and accountability, model drift, and data integration. Conclusions: Agentic AI shows transformative potential for auditing in terms of facilitating change from periodic to continuous auditing and reactive to proactive risk management. Successful adoption requires governance frameworks, transparent audit trails, specialized auditor know-how, and regulatory standards.

Purpose: This study examines how artificial intelligence (AI) adoption in banking accounting affects organizational resilience (OR) among Jordanian listed banks, and whether real-time decision intelligence (RTDI) mediates and digital leadership (DL) moderates those effects, including a moderated mediation pathway. Design/methodology/approach: A cross-sectional survey of 320 professionals drawn from all 14 Amman Stock Exchange-listed banks was analyzed via partial least squares structural equation modeling (PLS-SEM) using SmartPLS 4 with 5,000 bias-corrected bootstrap subsamples. AI was operationalized as a reflective–formative second-order hierarchical component model comprising four first-order dimensions: financial reporting, audit, risk management, and regulatory compliance. Findings: AI significantly enhances RTDI (β = 0.224, p = 0.008) and OR (β = 0.780, p &lt; 0.001), explaining 77.6% of OR variance. RTDI partially mediates the AI–OR relationship (β = 0.031, p = 0.043, VAF = 3.8%). DL exerts a negative moderation on AI→OR (β = −0.091, p = 0.001), indicating a ceiling/substitution effect. The moderated mediation is significant (β = −0.012, p = 0.027). Importance–performance map analysis (IPMA) identifies AI as the highest-priority construct for resilience improvement. Originality/value: This is the first study to integrate AI in banking accounting, RTDI, DL, and OR within a single moderated mediation model grounded in dynamic capabilities view (DCV), technology–organization–environment (TOE) framework, and upper echelons theory (UET) in an emerging-economy banking context. The negative moderation finding challenges the prevailing assumption that digital leadership unconditionally amplifies technology–performance relationships.

Objectives: This study examines the causal impact of mandatory sustainability assurance on the credibility of sustainability reports in an emerging market context. Specifically, it evaluates the regulatory reform introduced by the Jordan Securities Commission in 2023, which mandates external assurance for sustainability reports of firms listed on the Amman Stock Exchange with market capitalization exceeding JOD 100 million. The objective is to provide structured evidence on how regulatory intervention influences reporting quality, audit effort, and capital market outcomes. Data description: The dataset comprises 110 firms observed over the period 2020–2024, resulting in 550 firm-year observations. The data include measures of sustainability reporting quality based on SDG disclosures, assurance fees, number of assurance procedures, cost of equity, and proxies for information asymmetry. Additional variables capture corporate governance characteristics such as audit committee expertise and ownership structure. The dataset enables analysis of pre- and post-regulatory changes and supports causal inference using difference-in-differences and related methods. Limitations - Sample limited to Jordan - Time period restricted to 2020–2024 - Availability of sustainability disclosures varies across firms JEL Classification: M42, M48, G34, G38, Q56

AI is rapidly developing in auditing practices worldwide, but there is little empirical evidence on the link between AI and audit efficiency among emerging market banks. In addition, the organizational variables through which AI improves audit efficiency— for example, IT governance maturity— are not theoretically and empirically theoretically sound and empirically untested. This study explores the effect of AI adoption on audit delay in Jordanian banks and the altering role of IT governance maturity. The data for 2015–2024 are summarized in a balanced panel of 15 commercial and Islamic banks listed on the ASE, indicating 150 bank years. The normalized score of AI adoption is obtained through bilingual content analysis of annual reports, and a CoBIT-aligned composite index for IT governance maturity is measured by a standardized score of the bilingual content analysis of the annual reports. Four model specifications employ pooled OLS regression with Driscoll–Kraay standard errors robust to heteroskedasticity, autocorrelation, and cross-sectional dependence. This suggests that AI adoption significantly reduces audit delay (= 26.07, p = 0.01); an increased AI score = 4.06 fewer audit days, which translates into a 6.5% efficiency improvement; and an increase in one standard deviation in the AI score yields 4.06 fewer audit days, or a 6.5% improvement in accuracy. I conclude that the interaction between AI adoption and IT governance maturity is negative and significant: 3 = 6.22, p = 0.011, and banks with larger IT governance maturity significantly increase the efficiency of audit performance. The results are reliable when the year fixed effects are replaced with a COVID-19 control variable. The present study contributes the first archival evidence from emerging-market banks in the MENA region on the AI–audit efficiency nexus, incorporates Agency Theory, TOE and Institutional Theory, and emphasizes IT governance as a critical barrier to achieving an efficient use of AI in auditing. JEL Classification: M41, M42, G21, G34, O33

Researchers who measure how far a firm has integrated digital technology often count keywords in the annual report. Keyword counts do not separate a firm that uses a technology from a firm that only talks about it, and they work poorly when the report is filed in two languages. This paper presents a tool that codes annual report sentences for six digital technologies (artificial intelligence and machine learning, big data analytics, cloud computing, enterprise resource planning, smart accounting and reporting automation, and XBRL) and assigns each technology sentence to a commitment tier. The tool combines a bilingual dictionary screen, a fine-tuned XLM-RoBERTa model that decides whether a screened sentence describes the reporting firm using a technology, and a deterministic rule that gives the commitment tier from two auditable conditions: the firm is the grammatical subject, and a verb from a closed list acts on a technology term. The tool was built on 1,880 hand-coded sentences from Arabic and English XBRL filings of Amman Stock Exchange firms, without translation. In firm-grouped five-fold cross-validation, the hybrid reaches an F1 of 0.857 in English and 0.833 in Arabic on firms unseen in training, against 0.734 and 0.712 for the dictionary screen; on a held-out partition it also exceeds a zero-shot large language model given the same codebook. Applied to 326 paired filings, the tool shows that technology narrative is rare, concentrated, and consistent across the two language versions. The model, code, codebook and coded sentences are released for reuse and extension.

Adds a firm-grouped 5-fold cross-validation of the presence model over all 1,648 coded, non-artifact, non-duplicate sentences (88 firms, 259 positives), with the same settings as the held-out run. New files: training/cv_grouped.py results/results_cv_grouped.json (per-fold results; pooled out-of-fold F1 with 2,000-resample bootstrap CIs) results/pred_cv_oof.csv (out-of-fold predictions for all 1,648 sentences) Pooled out-of-fold F1 (95% bootstrap CI): English (n 829, 149 positives): hybrid 0.857 [0.811, 0.898]; model 0.828 [0.777, 0.872]; dictionary 0.734 [0.684, 0.777] Arabic (n 819, 110 positives): hybrid 0.833 [0.777, 0.880]; model 0.815 [0.757, 0.864]; dictionary 0.712 [0.651, 0.768] Hybrid minus dictionary: 0.123 [0.071, 0.181] English; 0.121 [0.067, 0.176] Arabic. All v1.0.0 files are unchanged. The released model weights are unchanged; the fold models are for evaluation only and are not released. Model weights: https://huggingface.co/ahmadomari/dti-coder-xlmr Live app: https://dti-coder.streamlit.app Ahmad Alomari and Anwar Allah Pitchay, School of Management, Universiti Sains Malaysia.

Firm-year panel (405 firm-years, 27 industrial firms listed on the Amman Stock Exchange, 2011-2025) of board of directors and CEO characteristics coded at the level of the person from the published annual reports under fixed coding rules: postgraduate share of the board (with PhD and Master shares), business-degree share, mean age and age dispersion of the board, CEO postgraduate degree, CEO business degree, CEO age, board size, CEO duality and the share of seats held by representatives of legal-person shareholders, together with the December market capitalisation and book equity from the ASE bulletins, a completeness flag and the analysis code. Financial statement data from S&P Capital IQ are not redistributed under the licence terms. Variable definitions are in README.txt.