@article{4596,
  author = {Qian Ding},
  title = {The Impact of Employee Performance Commitments on Corporate Earnings Management: A Decision Tree Approach based on Information Entropy},
  journal = {Journal of Intelligent Computing},
  year = {2025},
  volume = {16},
  number = {4},
  doi = {https://doi.org/10.6025/jic/2025/16/4/137-144},
  url = {https://www.dline.info/jic/fulltext/v16n4/jicv16n4_1.pdf},
  abstract = {This work explores the relationship between employee performance commitments and corporate earnings
management, particularly in the context of mergers and acquisitions (M&A). It highlights that while performance
commitments can align employee goals with corporate strategy, reduce information asymmetry,
and signal market confidence, they may also incentivize earnings management when targets are
unrealistically high. The study leverages a decision tree model based on information entropy to analyze this
dynamic using M&A data from 2009 to 2022. The model uses real earnings management as the dependent
variable and includes commitment duration, commitment amount, and several financial controls. Results
indicate a positive correlation between unrealistic performance commitments and heightened earnings
management. A case study of "XX Smart Enterprise" illustrates how excessive commitments led to significant
financial strain and potential earnings manipulation. The paper proposes an optimized commitment level of
approximately 30% to mitigate such risks. By integrating a generalized entropy increase algorithm into the
traditional decision tree framework, the model improves prediction accuracy and offers practical guidance
for setting realistic performance targets. The findings caution against overly ambitious commitments in
M&A agreements and emphasize the need for balanced incentive structures to prevent short term earnings
manipulation that could harm long term corporate health and stakeholder trust.},
}