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Enregistrement W332099910

Making the Invisible Visible: Identifying the Enablers of Future Value

2008· article· en· W332099910 sur OpenAlexaboutno aff
Bernard Marr

Notice bibliographique

RevueJournal of accountancy online/Journal of accountancy · 2008
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueIntellectual Capital and Performance Analysis
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésIntellectual capitalStructural capitalRelational capitalOrganizational capitalIndividual capitalBusinessFinancial capitalEconomic capitalValue (mathematics)Capital (architecture)Human capitalKnowledge managementEconomicsFinanceComputer scienceEconomic growth
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Recent research confirms that while most executives agree that intellectual capital is critical to the future success of their businesses, their approaches to measuring and managing this performance enabler are either poor or nonexistent. This has provided the impetus for the AICPA, in conjunction with CMA Canada and CIMA, to create a Management Accounting Guideline (MAG) on intellectual capital. Impacting Future Value: How to Manage your Intellectual Capital provides detailed guidelines across the following five steps of successful intellectual capital management. STEP 1: How TO IDENTIFY THE INTELLECTUAL CAPITAL IN YOUR ORGANIZATION Included in this step is an assessment of its value. Not all intellectual capital is automatically valuable to an organization. It is only valuable if it helps to deliver the organizational objectives. Intellectual capital value drivers can be identified by conducting interviews and facilitated workshops, or via mail or online surveys. Together with physical and financial capital, intellectual capital is one of the three vital resources of organizations. Intellectual capital includes all intangible resources that are attributed to an organization and contribute to the delivery of the organizational strategy. These intangible resources can be grouped into human, structural and relational capital. Once intellectual capital has been identified, its value can be assessed. When valuing intellectual capital, it should be kept in mind that its value depends on an organization's specific strategy and that intellectual capital dynamically interacts with and depends on other resources. STEP 2: How TO MAP THE INTELLECTUAL CAPITAL AND ASSESS ITS STRATEGIC IMPORTANCE A value creation map is a visual representation of an organization's unique strategy at a specific point in time. This means it has a limited life span and, as a consequence, must be revised regularly (usually annually). Every value creation map is unique to the current strategy of an organization, and no two value creation maps should be the same. This visual representation has two primary functions--to ensure that the strategy with all its intellectual capital value drivers is integrated and coherent, and to enable easy communication of the strategy and the role and importance of intellectual capital in delivering the strategy. STEP 3: HOW TO MEASURE INTELLECTUAL CAPITAL After identifying and mapping the intellectual capital value drivers, organizations can start measuring them. The aim of performance measures should be to provide meaningful information that helps reduce uncertainty about intellectual capital and enable learning. Measures ought to help managers and stakeholders make better-informed decisions that enable performance improvements. An excellent way of ensuring that an indicator is worth measuring is to establish the question(s) the indicators will help to answer. So-called key performance questions (KPQs) are designed to identify what it is managers want to know about the various intellectual capital value drivers. KPQs make sure any measure has a purpose and a clear aim. If there is no question that needs to be answered, there should not be a need to measure anything. For both existing or newly developed methods, it is important to assess whether it is possible to collect meaningful data and whether the data will help answer your questions. It is also important to assess whether the data warrant the costs and effort of measurement, which can be significant. If no meaningful data can be collected, or if the data are not really helping you answer the KPQ, or if the costs are not justified, then it is necessary to rethink and design different indicators. A model using key performance questions and key performance indicators is described in detail in the guideline. STEP 4: HOW TO MANAGE THE INTELLECTUAL CAPITAL IN YOUR ORGANIZATION Once intellectual capital is measured, it can be managed. …

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,109
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,000
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,003
Études des sciences et des technologies0,0010,000
Communication savante0,0000,006
Science ouverte0,0020,000
Intégrité de la recherche0,0000,002
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,047
Tête enseignante GPT0,281
Écart entre enseignants0,234 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2008
Routes d'admission1
Résumé présentoui

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