A model for resource allocation using operational knowledge assets
Notice bibliographique
Résumé
Purpose The paper seeks to develop a business model that shows the impact of operational knowledge assets on intellectual capital (IC) components and business performance and use the model to show how knowledge assets can be prioritized in driving resource allocation decisions. Design/methodology/approach Quantitative data were collected from 84 high‐tech federal contractors in the Washington DC metro area. Respondents in the target population were middle‐level and operations managers of business sectors holding positions as presidents, vice‐presidents, directors, engineering managers, operations managers, and analysts. Partial least squares (PLS) analysis was performed to develop a structural model between operational knowledge assets and IC components that maximizes explained variance for business performance. Operational assets were specified as formative constructs and IC and business performance were specified as reflective constructs. Findings A parsimonious conceptually sound model with significant measured variables and path coefficients was developed that explains almost 40 percent of the variance in business performance. The model shows both the interrelationships between the IC components that drive performance and the operational assets as levers for each IC component, respectively. Research limitations/implications The scope of the study was focused on the high‐tech federal contractors in the USA. However, the model can be applied and tested in different industry sectors. This would provide evidence of the different operational knowledge assets used as levers in different industry sectors. Practical implications Senior executives and chief financial officers in particular are constantly challenged with making the optimum investment decisions given their budget constraints. The model offers a tool for developing and evaluating different resource allocation decisions based on an organization's strategic intent. In addition, the model can be useful in evaluating merger and acquisition decisions. In evaluating target companies the model can be used to identify the core capabilities or competency areas that the target company is leveraging and assess the impact or integration potential for the acquiring company. Originality/value This is the first study in the field of IC that has adopted the use of formative indicators in specifying operational knowledge asset constructs. Previous research has focused on developing models with the use of proxy measures as reflective indicators. Therefore the emphasis so far has been on scale development. The use of formative items in this study fills both the business need and theory gap to understand better the causal relationships that exist between work and knowledge assets.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,005 | 0,007 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,002 |
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 source (Gemma direct ou Codex distillé), 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 ».