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Record W2035099834 · doi:10.1057/kmrp.2011.26

Absorptive capacity: a proposed operationalization

2011· article· en· W2035099834 on OpenAlex

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

VenueKnowledge Management Research & Practice · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsOperationalizationAbsorptive capacityDynamic capabilitiesPerspective (graphical)Knowledge managementExploratory researchComputer scienceManagement scienceAssimilation (phonology)Process managementBusinessSociologyEngineeringEpistemologySocial scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The concept of absorptive capacity has already been considerably studied from a theoretical perspective, but few, if any, attempts at operationalizing the concept have been studied in ways that would allow its full assessment. The more specific focus provided by the four dimensions identified in some recent literature – acquisition, assimilation, transformation and exploitation – opens up some promising avenues for operationalizing the concept. This exploratory research studies and describes case studies of ten innovative companies using a cross-sectional research design. In the first part of the article, we re-examine the concept of absorptive capacity in terms of dynamic capabilities and provide a review of the relevant literature. The second part describes the work accomplished to operationalize the concept of dynamic capability and analyses the possible relationship between the business strategies adopted by the companies studied and their particular strategic capacity.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.016

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 head0.204
GPT teacher head0.359
Teacher spread0.155 · 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