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Record W332099910

Making the Invisible Visible: Identifying the Enablers of Future Value

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

Bibliographic record

VenueJournal of accountancy online/Journal of accountancy · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Capital and Performance Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsIntellectual capitalStructural capitalRelational capitalOrganizational capitalIndividual capitalBusinessFinancial capitalEconomic capitalValue (mathematics)Capital (architecture)Human capitalKnowledge managementEconomicsFinanceComputer scienceEconomic growth
DOInot available

Abstract

fetched live from 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. …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.006
Open science0.0020.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.047
GPT teacher head0.281
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2008
Admission routes1
Has abstractyes

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Same venueJournal of accountancy online/Journal of accountancySame topicIntellectual Capital and Performance AnalysisFrench-language works237,207