From AI Policy to Corporate Strategic Change: Evidence on Firm-Size Heterogeneity from China’s AI Pilot Zones

Abstract

Artificial intelligence (AI) pilot-zone policies aim to expand firms’ technological opportunities, yet their relationship with changes in internal resource allocation remains unclear. Using 15,989 firm-year observations from 2377 nonfinancial Chinese A-share listed firms during 2016–2024, this study employs the Callaway–Sant’Anna staggered difference-in-differences framework to examine corporate strategic change (SC). SC is measured as an equally weighted index of standardized absolute annual changes in six accounting-based resource-allocation ratios, capturing adjustment magnitude rather than direction or performance. The pooled average treatment effect is −0.0162, with a 95% confidence interval from −0.0634 to 0.0310 and a normal-approximation minimum detectable effect of 0.0675 at 5% significance and 80% power, providing insufficient evidence of a detectable average effect. In contrast, the formal Small–Large difference in estimated effects is −0.0993, with a p-value of 0.0113. A joint test rejects equality of the six dimension-specific Small–Large contrasts, with plant and equipment newness showing the clearest difference after multiple-testing correction. Pre-treatment diagnostics vary across policy cohorts, and sensitivity analyses warrant caution in interpreting the estimates. These findings distinguish resource-allocation changes from technology adoption and business outcomes, providing an organizational perspective on heterogeneous firm responses relevant to sustainable digital transformation.