Abstract This research seeks to empirically explore the mediating role of governance quality on the nexus between generative artificial intelligence and macro-level financial performance. This empirical study employs cross-country panel data from 2021 to 2024, encompassing an analytical sample of nine emerging markets. Initially, various static panel data techniques were employed. Afterward, to alleviate potential endogeneity bias and support the reliability of the results, the one-step system GMM approach was implemented. The results reveal that while fixed-effects estimates indicate a partial mediating role of governance quality in the nexus between generative artificial intelligence and macro-level financial performance, the dynamic system GMM estimations support full mediation once endogeneity and path dependence are controlled for. Taken together, these findings underscore the central role of governance quality as the primary transmission channel through which AI readiness translates into macro-level financial performance. The novelty of this research is reflected in its application of mediation techniques to elucidate the relationship between generative AI and macro-level financial performance. Moreover, this study pays rigorous attention to offer multidimensional insights for regulators and policymakers to design solid regulatory frameworks that enhance the adoption of generative AI tools, uphold high governance standards, and ultimately strengthen macro-level financial performance.
Awwad et al. (Thu,) studied this question.