Organizational cultures across national boundaries: Results of a cluster analysis
Bibliographic record
Abstract
The recent increase in global business has resulted in new challenges for organizations. One of these challenges is to create a strong global culture while simultaneously adapting to local cultural expectations. This research explored how both globalization and local adaptations of organizational cultures may coexist by using cluster analysis. Organizational culture data were used to cluster organizations from ten different nations: Australia, Canada, Germany, Hong Kong, Italy, New Zealand, Singapore, South Africa, the United Kingdom and the United States. Two solutions were generated using hierarchical clustering. In the first solution, four clusters emerged, but two of them were single item clusters (organizations in Germany and South Africa). Following recommended procedure, organizations from these countries were removed from analysis and the cluster analysis was rerun. Two main clusters then emerged: one with Asian countries, and one with European, North American, and Australasian countries. Cluster profiling suggests that significant differences between the clusters were found in each of the organizational culture aspects used to form the cluster variate.
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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 | 0.004 | 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 | 0.001 | 0.002 |
| 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".