Virtuous or vicious cycles? <scp>T</scp> he role of divestitures as a complementary <scp>P</scp> enrose effect within resource‐based theory
Bibliographic record
Abstract
Research summary : Studies of how divestitures affect firm performance offer mixed results. This paper unpacks relationships between divestitures and subsequent performance, focusing first on the moderating role of prior performance and then on mechanisms through which divestitures by higher‐ and lower‐performing firms affect performance. The study suggests that divestitures can exacerbate weakness and reinforce strength: divestitures by lower performers improve profits but inhibit sales growth and tend to speed the firms’ exits as independent actors; by contrast, higher‐performing divesters invest in support of existing assets and gain new growth, while avoiding becoming acquisition targets. Most generally, divestitures help reduce constraints to changing a firm's resource base, which we refer to as a complementary Penrose effect. Managerial summary : Divestitures help both struggling firms and high performers free financial and managerial resources that they can reinvest in more productive uses. In doing so, divestitures reinforce the strength of high performers but may exacerbate weaknesses of struggling firms. Divestitures by lower performers improve their profits but inhibit their sales growth and increase the chances that the firms will be acquired. By contrast, higher‐performing divesters gain new growth by investing in support of existing and recently acquired assets and, by doing so, are less likely to become targets of acquirers who seek their productive assets. Thus, divestiture is part of a downward cycle for struggling firms but supports a virtuous cycle for superior firms.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".