Complicating Abandonment: How a Multi-Stage Theory of Abandonment Clarifies the Evolution of an Adopted Practice
Bibliographic record
Abstract
This article presents a process-model for the abandonment of a practice. This complements earlier research on adoption and abandonment by allowing for fluctuations in the level of commitment across time and by demonstrating the persistent role for both institutional pressure and performance-based concerns on the maintenance of a practice. It also provides a novel means for identifying differences in the method of abandonment through the introduction of a concept of decommitment. Further, it helps resolve the question of how firms respond when faced with conflicting internal and external evidence of the success of an adopted practice. Using the divestiture of unrelated business segments by 100 U.S. firms between 1970–96, I estimate post-adoption commitment to a practice and the likelihood of a given firm decommitting. I find that treating abandonment as a process clarifies the evolving role of institutional and performance-based concerns and helps identify when a given firm is more subject to either source of pressure. The implications of this approach and these findings for current research on resistance to adoption and de-institutionalization are explored in the conclusion.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".