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Record W6940681812 · doi:10.11575/prism/34052

Examining the Impact of Culture’s Consequences: A Three-Decade, Multi-Level, Meta-Analytic Review of Hofstede‟s Cultural Value Dimensions

2010· other· en· W6940681812 on OpenAlexvenueno aff

Bibliographic record

VenueLibraries and Cultural Resources (University of Calgary) · 2010
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsConceptualizationHofstede's cultural dimensions theoryBig Five personality traitsPersonalityValue (mathematics)Predictive powerBig Five personality traits and cultureVariety (cybernetics)Cultural diversity

Abstract

fetched live from OpenAlex

Using data from 598 studies representing over 200,000 individuals, we meta-analyze the relationship between Hofstede‟s (1980a) original four cultural value dimensions and a variety of organizationally relevant outcomes. First, values predict outcomes with similar strength (with an overall absolute weighted effect size of ρ=0.18) at the individual level of analysis. Second, the predictive power of the cultural values was significantly lower than that of personality traits and demographics for certain outcomes (e.g., job performance, absenteeism, turnover), but significantly higher for others (e.g., organizational commitment, identification, citizenship behavior, team-related attitudes, feedback seeking). Third, cultural values were most strongly related to emotions, followed by attitudes, then behaviors, and finally job performance. Fourth, cultural values were more strongly related to outcomes for managers (rather than students), older, male, and more educated respondents. Fifth, findings were stronger for primary, rather than secondary, data. Finally, we provide support for Gelfand, Nishii and Raver's (2006) conceptualization of societal tightness-looseness, finding significantly stronger effects in culturally tighter, rather than looser, countries.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.293
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.000

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 head0.067
GPT teacher head0.245
Teacher spread0.178 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2010
Admission routes1
Has abstractyes

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