Abstract
Background: Increasing adoption of algorithmic corporate governance systems (ACGS) has transformed monitoring, reporting, and managerial decision environments. Existing corporate governance literature examines institutional and board determinants of earnings management but has paid limited attention to how algorithmic governance interacts with managers' neurobehavioral states to shape manipulation choices. Methods: We report a mixed-method field experiment and archival study of 420 mid- to senior-level financial managers from 210 publicly listed firms across three jurisdictions. Firms were classified by ACGS adoption intensity. Behavioral data were collected through incentivized earnings-reporting tasks, validated psychometric instruments (cognitive reflection, moral disengagement, risk preference), and physiological markers of stress (resting heart rate variability). Archival manipulation measures combined discretionary accruals and real activities manipulation proxies. Multilevel regressions, interaction tests, and mediation analyses were employed. Results: Higher ACGS intensity is associated with lower accrual-based manipulation but greater incidence of real activities manipulation, conditional on neurobehavioral variables. Specifically, low cognitive reflection and high moral disengagement weaken the suppressing effect of ACGS on accrual manipulation and amplify the shift toward real activities manipulation. Physiological stress moderates these relationships: managers with lower HRV (higher stress) are more likely to substitute real activities manipulation when operating under algorithmic oversight. Robustness checks, including instrumenting ACGS adoption with industry-level uptake and alternative manipulation metrics, confirm results. Conclusions: Algorithmic governance reconfigures the opportunistic landscape rather than uniformly constraining earnings manipulation. Neurobehavioral states of managers critically determine the direction and magnitude of manipulation under ACGS. Governance prescriptions should combine algorithmic transparency, behavioral screening, and redesign of performance metrics to mitigate substitution effects.