Operations‐Related Structural Flux: Firm Performance Effects of Executives’ Appointments and Exits
Bibliographic record
Abstract
Conceptualized as a meta‐construct, operations‐related structural flux (ORSF) refers to appointments and exits—voluntary or involuntary—of operations‐related executives, to and from the firm. This research leverages the contingency theory perspective to show that ORSF’s influence on firm performance is contingent on contextual circumstances of such executive changes, specifically, appointments and exits—voluntary or involuntary. Examining executive turnover data from North American public firms between 2000 and 2016, the authors find that the firm‐level context of operations executives’ turnover is consequential for firm performance. On average, operations appointments are adaptive, but operations exits, including those due to both voluntary and involuntary reasons, disrupt firm performance. However, parallel effects are not evident for marketing‐ and finance‐related appointments and exits. Furthermore, our study reveals that exit of one operations executive hurts firm performance (measured in terms of Tobin’s q) by 3.3%. A post‐hoc analysis finds that firm performance of the sample firms that witness involuntary operations‐related exits (IVOpE)is, on average, 9.2% lower than that of the sample firms that do not witness IVOpE. These results indicate the outsized influence of operations‐related executives, who collectively are generally responsible for much of a firm’s budget, workforce, resources, structures, and capabilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".