Modeling For Valuing Knowledge as Perceived by Business Managers Using Statistical Tools
Bibliographic record
Abstract
Knowledge is a valuable asset as it brings success and sustainability to the organizations. Till recently, the value of an organization is determined from its financial statements. These statements are historical in nature and contain the book value of physical assets, hence do not depict the true worth of an organization. The future revenue/profit from the organization depends upon its capability to make best use of its assets. This depends on the quality of knowledge an organization possess and its capability to use that knowledge asset. Therefore, knowledge is the most important asset in an organization. However there is no financial statement or business document that shows the volume and value of knowledge present in the organization. Hence, it is critical to determine the value of knowledge to ascertain true worth of an organization.This research study attempts to present factors that influence the value of knowledge during its life cycle. Data were collected through interviews and questionnaire instrument was used to get subsequent data from 521 business managers working in various industries. The collected data was subjected to various statistical tools to evaluate the factors and their hypothesis. The twenty two factors identified initially were first analyzed for their verification and authenticity. Later each item was regrouped through the Rotated Component Matrix analysis – first order for meaningful set of factors. Based on the result of second order Rotated Component Matrix analysis, all the newly identified factors were finally grouped into two groups of factors that influences the value of knowledge. These groups were: ‘Efforts’ and ‘Business’. The integration of ‘Efforts’ and ‘Business’ factors forms the Knowledge Value Wheel (KVW) that helps in defining the “Knowledge Value Line” (KVL). The KVL depicts the value of knowledge at any given time. The KVL and KVW combines to form the “Knowledge Value Life Cycle” (KVLC).The findings will help further research in the area of knowledge management. Managers would be able to differentiate most valuable and useful knowledge asset for effective management. Need for further R&D on critical knowledge can be identified. It would also be beneficial to the investors in determining the true worth of an organization in terms of its knowledge asset.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".