The Impact Of Performance Expectation Gap On Corporate Strategic Change—Evidence from Listed Companies in the IT Industry
Bibliographic record
Abstract
Based on the panel data of Chinese listed companies in the information technology industry from 2007 to 2018, this paper uses a fixed-effect model to study the relationship between corporate performance expectation gap and strategic change and analyzes the moderating effect of private benefits of management control and equity incentive. It is found that the greater the gap between corporate performance expectations is, the lower the frequency of corporate mergers and acquisitions is, and the higher the frequency of corporate asset divestment is. Further research finds that private benefits of management control weaken the positive correlation between corporate performance expectation gap and asset stripping frequency. Equity incentive strengthens the negative correlation between corporate performance expectation gap and corporate mergers and acquisitions frequency, and the positive correlation between corporate performance expectation gap and corporate asset stripping frequency. Based on this, when enterprises carry out strategic change, enterprises should choose the direction of strategic change according to the degree of performance expectation gap, and promote the effective realization of strategic change by improving the governance of the board of directors and optimizing the management incentive mechanism.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".