Intra-Cultural Variation, Zone of Acceptance and Managerial Discretion: A Theoretical Discussion
Bibliographic record
Abstract
This paper examines the theoretical relationship between intra-cultural variation and managerial discretion. Research into the degree of discretion, or latitude of actions, has primarily focused on the individual-, organizational-, and industry-level factors, which either allow or constrain executives to take strategic actions. Despite, the recent attempt to discover the impact of national culture, mainly values, on managerial discretion, culture has been studied on an aggregate level by assuming spatial homogeneity within a country. However, recent evidences have shown that intra-cultural variation could be as salient as or sometimes even more than inter-country variation, yet there has been no discussion on its potential association with managerial discretion. As such, we address this gap and investigate the relationship of this cultural aspect with managerial discretion. Using institutional, stakeholder and upper echelons theories, our study proposes a strong relationship between intra-cultural variation and managerial discretion. Therefore, our study contributes to the strategic management and culture literature by providing a more nuanced understanding of such relationship and most importantly by introducing a new national construct that could play an important role in the strategic decision making of business executives.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".