Bibliographic record
Abstract
Purpose The purpose of this paper is to develop propositions for empirical validation regarding appropriate management planning and control systems (MPACS) in knowledge‐intensive organizations. Design/methodology/approach The propositions were developed from interviews with members of a knowledge‐intensive virtual organization that is known for its innovative practices regarding intellectual capital (IC) development and surveys from low to middle range managers, using a semi‐structured questionnaire, from a variety of companies. Trends in responses permitted us to identify issues of importance in developing innovative MPACS for knowledge‐intensive companies. Findings The paper proposes that two variables, the level of IC intensity and the uncertainty of knowledge, are important for determining the degree of adaptive versus generative characteristics that an organization's MPACS should contain. Regarding IC, the paper further proposes that organizations must give careful thought to ensure that both adaptive and generative characteristics are aligned with four MPACS elements of focus, commitment, capability, and learning. Originality/value As organizations develop programs to realize the potential from their intellectual capital, many fail to develop MPACS that are appropriate for knowledge‐intensive environments. MPACS should support knowledge creation, as well as knowledge sharing, and contain elements of both adaptive and generative systems.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".