Modeling For Valuing Knowledge as Perceived by Business Managers Using Statistical Tools
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».