AI adoption divergence and ESG: rethinking corporate financialization and financial performance

This study explores the impact of financialization based on assets, debt, and income on the financial performance of Chinese non-financial listed firms in the A-share market, using a sample of 40,365 firm-year observations from 2010 to 2023. We applied fixed-effects, two-stage least squares models (IV-2SLS) using instrumental variables, and PSM-DID models, demonstrating that financialization based on assets and debt has a significantly negative impact on firm profitability. Financialization based on income, however, does not have a significant effect. Using signalling theory, agency theory, and resource-based views, the study also develops an integrated framework whereby divergence in the discourse surrounding AI and actual AI adoption (primary mechanism) is moderated, and ESG practice (secondary mechanism) mediates the effects of financialization on performance. The findings show that those firms that have a greater divergence between AI discourse and AI expenditure have lower financial performance, while superior ESG practice ameliorates the negative effects of financialization. Overall, the findings show that excessive financialization is associated with adverse performance outcomes, and the firm’s AI capability and ESG practice represent strategic forms of corporate performance.