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Record W2583690728 · doi:10.5430/bmr.v6n1p42

Intra-Cultural Variation, Zone of Acceptance and Managerial Discretion: A Theoretical Discussion

2017· article· en· W2583690728 on OpenAlexvenueno aff
Moustafa Haj Youssef, Ioannis Christodoulou

Bibliographic record

VenueBusiness and Management Research · 2017
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsnot available
Fundersnot available
KeywordsDiscretionVariation (astronomy)SalientConstruct (python library)StakeholderPositive economicsHofstede's cultural dimensions theoryPublic relationsBusinessPolitical scienceAccountingSocial psychologyEconomicsPsychologyLaw

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.981
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.095
GPT teacher head0.429
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2017
Admission routes1
Has abstractyes

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