A framework for prioritization of intellectual capital indicators in R&D
Bibliographic record
Abstract
Purpose The purpose of this paper is to present a framework for prioritizing intellectual capital (IC) indicators, as well as suggesting key improvement areas by using the Delphi approach and analytic hierarchy process (AHP) analysis. Design/methodology/approach The effectiveness of the framework is demonstrated through the case study of a public sector research and development organization. Findings This paper identifies three major characteristics of the framework: weighing indicators that should be based on an organization's strategies and contexts; employing multiple processes (Delphi and AHP approaches) which can overcome the limitation of a single methodology; and providing a visual map that can help management identify which indicators and related activities need attention and should be improved. Originality/value This research contributes to the literature and practices in several ways. First, this paper provides a practical and operational guideline on how to engage in IC management efficiently. Second, the authors try to integrate IC management into traditional management tools (e.g. quality management) by employing the concept of an operational feedback process and three screening processes. Third, this paper tests the possibility of using a Delphi approach in prioritizing IC 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 | 0.055 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.024 | 0.010 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".