Bibliographic record
Abstract
Purpose To examine whether the “big five” personality factors operate similarly from a psychometric perspective across dissimilar cultures. Design/methodology/approach Managers from the USA and Japan were administered a work‐oriented measure of the big five and overall assessment ratings were collected. Independent groups t‐tests were used to examine mean differences in personality scores across samples. Factor analysis was used to examine the structure of the big five across samples. Relative importance analyses were used to examine whether assessors across samples differentially weighted the big five in arriving at overall assessment ratings. Findings Big five personality dimension scores were significantly higher in the US sample compared to the Japanese sample. Across both samples, relative importance analyses revealed extraversion to be the most important correlate of predicted job performance, whereas conscientiousness was the least important correlate of predicted job performance. Research limitations/implications Three limitations existed: relatively small sample size for the Japanese sample (n=410) compared to the US sample (n=3,458); scarcity of Japanese demographic information makes interpretation of results due to culture less certain; and follow‐up data on actual hiring decisions would enable additional interpretations of the data to be made. Practical implications Results suggest that: the Five Factor Model of personality is rather robust across cultures, samples, and types of instruments, possible response biases across cultures should be taken into account when developing norms and setting cutoffs. Originality/value Although a consistent response bias is evidenced across the USA and Japan, the Five Factor Model of personality remains robust and what makes for an effective manager appears to be consistent across cultures.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".