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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 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)0.002
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 score0.709
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
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 head0.039
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 teacher head, 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

Citations7
Published2023
Admission routes2
Has abstractyes

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