Learning Organization Impact on Internal Intellectual Capital Risks: An Empirical Study in the Jordanian Pharmaceutical Industry Companies
Bibliographic record
Abstract
The intellectual capital with its different dimensions (Social Capital, Structural Capital, Human Capital, Creative Capital and Relational Capital) is a part of the strategic assets, which helps organizations to survive in the changing globalization environment. The intellectual capital is exposed to many risks at the level of internal environment, which require to be studied and to know their origins and diagnose their causes in order to dealing with its. The continuous learning, supporting leadership, organizing social activities that support self-learning and collective learning that contribute to the enhancement of knowledge leading to generate creativity as a part of the most important handling factors of the intellectual capital risks. This study aims to clarify the contribution mechanism of learning organization to dealing with internal intellectual capital risks. As well as, making recommendations to the decision-makers in this sector, which would contribute to the development of their organizations and help to convert them into learning organizations, and contribute to achieve their objectives efficiently and effectively. Four companies were chosen from (24) companies as a sample for this study. This study found a set of results focusing on that the learning organization can work on dealing with of intellectual capital risks with its different kinds through practicing the philosophy of learning organization. The study also found a set of recommendations that serve the purpose of the study.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".