The Impact of Intellectual Capital on Job Performance based on Faculty Members’ Perceptions at Universities
Bibliographic record
Abstract
Intellectual capital considers intangible efforts that complete each other. The conversion of efforts and knowledge into valuable assets has come to be known as intellectual capital. This study aims to examine the impact of intellectual capital on the job performance of faculty members at universities. The study used a cross-sectional design. The study population was the academic staff at Jordanian universities. The participants were chosen randomly from different faculties. The study relied on a quantitative method, and the tool for data collection was a questionnaire. The results found that the intellectual capital at universities was high, and the job performance of these universities was also high. In addition, the study found a highly positive impact of intellectual capital on job performance. This study discovers the impact of intellectual capital on the job performance of faculty members at universities. It also draws attention to the importance of intellectual capital in enhancing university performance. This study is useful for decision-makers at universities to maintain their performance and improve the higher-education system in Jordan.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".