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Record W4323538990 · doi:10.1080/00472778.2023.2182442

Agility and improvisation in Ontario’s craft breweries: Capabilities for constraints-based innovation

2023· article· en· W4323538990 on OpenAlexaffabout
Nadège Levallet, Suchit Ahuja, Corey Wood

Bibliographic record

VenueJournal of Small Business Management · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsUniversity of GuelphConcordia University
Fundersnot available
KeywordsImprovisationCraftBusinessContext (archaeology)MarketingIndustrial organizationDynamic capabilitiesKnowledge managementProcess managementComputer science

Abstract

fetched live from OpenAlex

In the context of most small businesses, innovativeness is critical for survival. However, small businesses often lack resources and are limited in their ability to influence external constraints. Consequently, they need to innovate in unique and often limited ways. While constraints-based innovativeness is discussed in emerging economies, we know little about how it occurs in advanced economies like Canada. This study uses a case study method in the craft brewery industry to examine different paths to constraints-based innovativeness through two capabilities, namely organizational agility and organizational improvisation capability. Results indicate an important but limited role for these capabilities for constraints-based innovativeness in the context of craft brewing, but also uncover different paths for development and evolution of innovativeness. This opens future research opportunities to study innovativeness, agility and organizational improvisation capability in small businesses facing resource constraints.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score0.762

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.006
Scholarly communication0.0060.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.039
GPT teacher head0.241
Teacher spread0.201 · 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 designQualitative
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

Citations7
Published2023
Admission routes2
Has abstractyes

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