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Record W1989821121 · doi:10.1108/01443571211226515

The role of relative absorptive capacity in improving suppliers' operational performance

2012· article· en· W1989821121 on OpenAlexaffabout
Haithem Nagati, Claudia Rebolledo

Bibliographic record

VenueInternational Journal of Operations & Production Management · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsAbsorptive capacityContext (archaeology)Knowledge transferKnowledge managementBusinessOriginalityDimension (graph theory)Survey data collectionValue (mathematics)Structural equation modelingComputer scienceProcess managementQualitative research

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to examine the link between relative absorptive capacity and suppliers' operational performance. Design/methodology/approach The paper uses structural equation modelling of survey data from 218 Canadian manufacturers referring to a particular relationship with one of their customers. Findings Results suggest that only the first dimension of the relative absorptive capacity – knowledge sharing routines – influences the knowledge transferred from the customer to the supplier. Knowledge transfer acts as a mediator between knowledge sharing routines and the supplier's operational performance improvement. Research limitations/implications The absence of a significant association between the second dimension of relative absorptive capacity – overlapped knowledge bases – and knowledge transfer is a surprising result that should be further investigated. Originality/value This appears to be the first study to operationalise and empirically test relative absorptive capacity and its consequences in the particular context of customer‐supplier relationships.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.039
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.232
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations63
Published2012
Admission routes2
Has abstractyes

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