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Feast in Time of Plague: Capabilities and Firm Performance in a Context of Sector Decline

2012· article· en· W2901548573 on OpenAlexaffabout
Mihai Ibanescu, Serghei Floricel, Jorge Niosi

Bibliographic record

VenueAcademy of Management Proceedings · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsDynamic capabilitiesRelevance (law)Context (archaeology)Manufacturing sectorCompetitive advantageIndustrial organizationBusinessPlague (disease)EconomicsMarketingLabour economicsPolitical scienceGeography

Abstract

fetched live from OpenAlex

We used various streams of managerial and economic literature with the aim to better understand the determinants of firm performance under the conditions of a persistent decline in the munificence of the competitive environment. We investigated the roles of both dynamic and operational capabilities of the firm, and the role of sectoral barriers, on the performance of the firm. Our analysis is based on a study including some 12000 manufacturing enterprises in Quebec, Canada. We developed some new measures of capabilities, barriers and performance. Our hypotheses about a different role of dynamic or operational capabilities in a declining sector are partially confirmed. The results may contribute to a better understanding of the relevance and limits of theories explaining competitive advantage and firm performance in declining environments

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)0.003
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score0.302

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.003
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.222
Teacher spread0.193 · 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 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

Citations0
Published2012
Admission routes2
Has abstractyes

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