Managerial Discretion: The Relationship Between Investments in R & D and Performance -Revisited: Case of Tunisian Enterprises
Bibliographic record
Abstract
This article proposes a re-reading of the role of R & D investments in the performance equation when a manifestation of an opportunistic inclination on the part of managers occurs. Often, the manager and the owners express different expectations regarding investment choices in R & D. Although shareholders are looking for performance, the manager sometimes has a tendency to broaden his managerial discretion. The choice of R & D investments could thus reinforce managerial entrenchment. We will demonstrate the moderation exercised by managerial discretion on the R & D / Performance relationship.Our empirical validation will be put to examination against the Tunisian context by deploying various tests. We come to grips with the characteristics of the Tunisian context through an empirical study of 75 companies observed over a 7 -year time lapse, i.e. from 2008 to 2014. The explanatory analysis was an opportunity to highlight the existence of a moderation exercised over the main relationship dealt with.
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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 | 0.001 |
| 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".