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.

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.

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.