An Empirical Investigation on the Appointments of Supply Chain and Operations Management Executives
Bibliographic record
Abstract
This paper provides empirical evidence on the performance effects and choice of appointments of supply chain and operations management executives (SCOMEs). The analysis is based on a sample of 681 SCOME appointments that were publicly announced during the 2000–2011 period. We find that the stock market reaction is positive on the day of the announcement. Categorizing the SCOME appointments as new or old and insider or outsider, we find that the market reaction for newly created SCOME positions is positive. The market also reacts more positively when a SCOME is an outsider rather than an insider. The strongest positive reaction is observed when outsiders are hired for newly created SCOME positions. We find evidence of both poor stock price performance and poor operating performance in the period preceding the appointment of new SCOMEs. New SCOME appointments are not followed by an immediate improvement in stock price and operating performance. However, there is no further decline in performance, suggesting that the decline observed in the preappointment period does not continue after the new SCOME is appointed. We also find that the likelihood of a SCOME being an outsider is greater for firms that are smaller, operate in more concentrated industries, and have experienced poor prior performance. This paper was accepted by Serguei Netessine, operations management.
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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 | 0.015 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".