Beyond numbers: The governance role of data assets in real earnings management.

Clinical Snapshot

Key Findings

• P Value: Not reported in abstract; described as statistically significant and robust to IV and PSM sensitivity analyses

• Effect Size: Not reported in abstract; direction of association is negative (data assets associated with lower REM)

• Primary Outcome: Negative association between data assets (measured via deep learning textual analysis) and real earnings management (REM) composite score among Chinese A-share listed firms, 2010–2023

• Nnt Or Sensitivity: Not applicable (observational association study); no NNT, hazard ratio, or diagnostic accuracy metrics reported. Economic magnitude of REM reduction per unit increase in data asset score is not quantified in the abstract.

• Confidence Interval: Not reported in abstract

Clinical Application

The textual analysis methodology using deep learning is technically sophisticated and would require significant data science infrastructure to replicate. The REM measurement framework is well-established and feasible for accounting researchers. Direct application to clinical practice is not feasible or appropriate. This study has no direct applicability to Australian clinical practice, PBS formulary decisions, or TGA regulatory processes. For Australian healthcare context, the findings are tangentially relevant to: (1) ASX-listed healthcare companies and their financial reporting governance; (2) hospital and health network boards considering digital transformation investments and their governance externalities; (3) RACGP or AHPRA-regulated entities with corporate governance obligations. However, the Chinese institutional context (CSRC regulation, state ownership, A-share market structure) differs fundamentally from the ASX environment governed by ASIC, ASX Listing Rules, and the Corporations Act 2001. Any extrapolation to Australian healthcare corporate governance would require significant contextual adaptation and independent validation. Not directly applicable to clinical populations. The study population comprises Chinese A-share listed firms. Potential relevance exists for healthcare administrators, hospital boards, and health system CFOs considering digital infrastructure investments and their implications for financial reporting integrity in healthcare organisations.

Abstract

While data assets are increasingly recognized as strategic resources in the digital economy, their implications for financial reporting quality remain underexplored. Employing a textual analysis approach based on deep learning techniques to construct a measure of data assets, this study investigates their impact on real earnings management (REM) using a sample of Chinese A-share listed firms from 2010 to 2023. We document a negative association between data assets and REM. Our findings remain robust after addressing endogeneity concerns using instrumental variables based on regional digital infrastructure and propensity score matching. Mechanism analyses reveal that data assets mitigate REM through two distinct channels: curbing managerial short-termism and strengthening external monitoring via increased analyst coverage. Cross-sectional tests indicate that this governance effect is more pronounced among firms facing earnings pressure. Furthermore, data assets primarily constrain cost-side manipulation strategies, specifically abnormal production costs and discretionary expenditures. Finally, we show that the reduction in REM driven by data assets contributes to enhanced long-term firm value. Collectively, these findings uncover a novel governance effect of data assets, suggesting that digital infrastructure investments yield positive externalities for corporate financial behavior.

References

1. 1.Zhang, M., Qian, H., & Ma, T. (2026). Beyond numbers: The governance role of data assets in real earnings management. PLoS ONE. https://doi.org/10.1371/journal.pone.0351687
2. 2.Roychowdhury, S. (2006). Earnings management through real activities manipulation. Journal of Accounting and Economics, 42(3), 335–370. https://doi.org/10.1016/j.jacceco.2006.01.002