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Record W2153779956 · doi:10.1016/j.jom.2009.01.002

The effects of innovation–cost strategy, knowledge, and action in the supply chain on firm performance

2009· article· en· W2153779956 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

VenueJournal of Operations Management · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic Procurement and Policy
Canadian institutionsNexen (Canada)
FundersMichigan State University
KeywordsSupply chainBusinessIndustrial organizationIntellectual capitalCompetitive advantageAction (physics)Resource (disambiguation)Knowledge value chainKnowledge managementValue (mathematics)MarketingOrganizational learningComputer science

Abstract

fetched live from OpenAlex

Abstract Despite the importance of supply chains within today's economy, we know little about how the knowledge of supply chains can contribute to superior performance at the firm level. Building on the resource‐based view, knowledge‐based view and strategic choice theory, we develop hypotheses linking two knowledge‐driven supply chain phenomena (i.e., knowledge development capacity and intellectual capital), innovation–cost strategy, and action to firm‐level performance. Using survey and archival data from 489 firms, we found that performance is influenced by how well knowledge development capacity and intellectual capital efforts complement alternative chain strategies. More specifically, each strategy type requires different constellations of knowledge development capacity and intellectual capital to enhance action and create superior firm performance. These results highlight the importance of supply chain phenomena for firm‐level performance, and more broadly, the value of supply chains as a competitive weapon in contemporary firms.

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)0.001
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 score0.689
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.285
Teacher spread0.264 · 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