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Record W2121928689 · doi:10.1108/02756660910987608

Leveraging intangibles: how firms can create lasting value

2009· article· en· W2121928689 on OpenAlex
Alain Lapointe, Yan Cimon

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Business Strategy · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Capital and Performance Analysis
Canadian institutionsUniversité LavalHEC Montréal
Fundersnot available
KeywordsLeverage (statistics)BusinessOriginalityDynamic capabilitiesValue (mathematics)Competitive advantageCompetition (biology)Industrial organizationKnowledge managementPerspective (graphical)Value creationMarketingComputer scienceCreativity

Abstract

fetched live from OpenAlex

Purpose Firms are increasingly confronted with a complex and dynamic competitive environment. The purpose of this paper is to shed some light on the way firms can cope with – and succeed in – such an environment. Design/methodology/approach The article examines major factors that are driving a global structural shift toward increased global competition. After identifying the difficulties behind the management of value creation, it focuses on the specific role of intangibles with a view to building a responsive firm. Findings It is found that intangibles are the key to sustaining value creation in a complex and dynamic environment. Following this, some consideration is given to the elements that help build responsive firms. The paper concludes by proposing actionable ways for managers to leverage intangible‐based practices. Originality/value The case for leveraging intangibles is advanced through a mix between an international business perspective and the combined role of knowledge and cluster‐like environments. Numerous real‐world examples help substantiate the analysis.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.709

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.222
Teacher spread0.196 · 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