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.

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 < 0.01) whereas club revenue is the most important predictor of club market valuation (max 0.009, p < 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.

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.

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.

The paper explores how Green digital technologies (GDTs) - ERP systems, cloud, 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). On the basis of institutional isomorphism theory, we examine the relationship between the coercive, mimetic and normative institutional pressures and adoption of green technology interaction on the sustainability reporting practices. On the basis of panel data of 30 ASE-listed industrial companies during the period of 20202024 (N = 146 firm-year observations), we use pooled OLS and random-effects frameworks characterized by strong clustering of standard errors. Findings show that Green Digital Technology Index has a positive and significant agreement with the ESG disclosure scores (0.019; 0.024; 2.486, p value 0.019; 2.507, p value 0.024), with adopting firms having an average score of 1.73 higher. Its impact has been the most significant to the environmental aspect ( = 3.460, 0.074) = 0.074. Although institutional pressures fail to modulate the GDT-disclosure relationship, mediation analysis shows that institutional pressure is also a powerful predictor of GDT adoption (0.098, p 0.100), indicating that institutional forces play the role through technology adoption. The quality of disclosure has a negative relationship with CEO duality ( -4.863, p < 0.001). The results validate the assumption that the green digital technologies are a transmission mechanism where institutional pressures are converted to an enhancement of sustainability disclosure in the emerging markets.

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 < 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.