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Record W3101582194 · doi:10.1177/1465750320969621

Bricolage and MSEs in emerging economies

2020· article· en· W3101582194 on OpenAlexaff
Amon Simba, Nathanael Ojöng, George Kuk

Bibliographic record

VenueThe International Journal of Entrepreneurship and Innovation · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsYork University
Fundersnot available
KeywordsBricolageMainstreamContext (archaeology)Emerging marketsConceptual frameworkIndustrial organizationEconomicsScholarshipBusinessEntrepreneurshipMarketingSociologyEconomic growthPolitical scienceMacroeconomics

Abstract

fetched live from OpenAlex

This conceptual paper focuses on bricolage and it pays particular attention on the context of micro and small enterprises (MSEs) in resource-constrained environments – a common feature of most emerging economies. Knowledge about the underlying factors that determine bricolage as a common practice among MSEs operating in emerging economies is yet to advance and develop within the mainstream entrepreneurship literature. Much of this scholarship tends to focus on multi-national enterprises (MNEs) in advanced economies and it discusses bricolage as their strategic choice. Such an approach has led to a lack of meaningful theoretical paradigms for defining the business approaches MSEs adopt as a way of mitigating their perennial operational issues inherent in their environment. Thus, in this conceptual paper, which adopts a scoping review approach, we study the constructs of bricolage particularly their application in MSEs operating in emerging economies. From our analysis a fresh deterministic model mapping out the causal factors that give rise to bricolage behaviour in MSEs that operate in difficult conditions emerged. Thus, we contribute to entrepreneurial behaviour theories by identifying distinctive business methods MSEs adopt to withstand operational difficulties inherent in their 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 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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.221

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.033
GPT teacher head0.252
Teacher spread0.219 · 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

Citations54
Published2020
Admission routes1
Has abstractyes

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