Intellectual capital management: pathways to wealth creation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The management of intellectual capital (IC) within organizations depends on appropriate organizational structures and characteristics. This paper seeks to argue that certain structural, cultural, and climate characteristics will lead to more effective IC management. Design/methodology/approach The paper reviews the theoretical and empirical IC literature, as well as the literatures regarding organic environments, trust, participative decision making, and creative renewal processes, to develop a model relating to the antecedent conditions necessary for the management of IC. Findings The model developed will assist researchers in the identification and exploration of variables linked to the effective management of IC within organizations. Practical implications It is concluded that managers of organizations need to create organic structures, build trust with employees, encourage creative renewal, and develop participative decision‐making processes. Originality/value By integrating several fields of the literature that relate to IC management, the paper suggests propositions that deserve future research consideration.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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 it