Intellectual capital: Evidence from banking industry
Bibliographic record
Abstract
This paper investigates different components on intellectual capital including human capital, structural capital and customer capital in banking industry in city of Salmas, Iran. The study uses the questionnaire developed by Intellectual capital: an exploratory study that develops measures and models. Management Decision, 36(2), 63-76.] to measure the effects of human capital. The questionnaire consists of 42 questions and all of them are designed in Likert scale. Cronbach alphas for human capital, structural capital and relationship capital were calculated as 0.79, 0.76 and 0.72, respectively. The implementation of Kolmogorov-Smirnov test has indicated that the data were normally distributed. Using t-student test, the study determined that while management team did not pay enough attention on human capital, there were some statistically significant evidence that social and relationship capitals gained good attention.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".