The Extent of Using Intellectual Capital Criteria at Jordanian Public Shareholding Companies - A Field Study from the Jordanian Shareholding Companies’ Point of View
Bibliographic record
Abstract
Intellectual capital or knowledge has become a lifeline for all business organizations and companies. It plays a key role in the survival and the continuation of these organizations. Companies need to report their competitive position in the business environment, but organizations and companies find it difficult to measure for intellectual capital, as well as to define intellectual capital and the accounting methods to measure and display it in financial statements. The process of measuring intellectual capital and disclosure represents a major challenge for accountants. There is an urgent need to find measured accounting standards and develop the ones existing for some elements of this capital as the decision makers at the present time are in dire need of information on the elements of intellectual capital and the cost of these items and returns achieved by each element. Therefore, this research tried to review the theories regarding the concept of intellectual capital and accounting mechanisms that could be adopted to measure and present it. In addition to this, a special questionnaire for major companies in Jordan was prepared to find out their views about the importance and efficacy of this important resource. One of the most important research recommendations is the need for accountants to be aware and understand the nature and importance of intellectual capital in creating competitive value for shareholding companies in all their types and the need to develop models for the measurement of intellectual capital in companies that combine the indicators of financial and non-financial indicators.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".