The formation of green strategies in Chinese firms: matching corporate environmental responses and individual principles
Bibliographic record
Abstract
Abstract This study examines how Chinese firms began responding to worsening environmental concerns in the late 1990s. Combining predictions from control theory, escalation of commitment, and goal theory, we seek to explain how leaders' cognitions shape the formation of novel responses to the value‐laden issue of corporate greening. We propose an iterative model that links leaders' principles with corporate actions and test it using survey data gathered from 360 firms. The model views strategy organically, as a set of adaptive goals and behaviors, and highlights the role of systemic and local feedback loops in strategy formation. We find that top executives who champion new strategic initiatives monitor early success or failure, and adjust their efforts to match early performance feedback. Perceptions of satisfactory performance strengthen leaders' efforts towards their initial target, while perceptions of unsatisfactory performance diminish them. This feedback relationship is invariant throughout favorable or unfavorable expectancies of success, contrary to the contingent prediction of control theory. The model also examines how top‐down and bottom‐up strategic initiatives combine to help firms maintain a positive momentum of change when champions' efforts decline in the face of premature failure signals. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".