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Record W2766412855 · doi:10.5465/ambpp.2017.284

Different paths to the same Business: A Micro-level view of Entrepreneuring via Replication

2017· article· en· W2766412855 on OpenAlexaff
Patrick D. Shulist, Oana Branzei

Bibliographic record

VenueAcademy of Management Proceedings · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsWestern University
Fundersnot available
KeywordsPhenomenonEntrepreneurshipMicro levelReplication (statistics)NeglectMacro levelBusinessNew VenturesQualitative researchMarketingIndustrial organizationEconomicsMicroeconomicsEconomic systemSociologyEconomic impact analysisPsychologyFinanceSocial science

Abstract

fetched live from OpenAlex

Entrepreneurship research has traditionally focused on far-from- equilibrium economic processes. Through doing so, significant insight have been made into the nature of innovative discovery and creation opportunities. However, this focus led to unintentional neglect of near-equilibrium entrepreneurship – entrepreneurship happening in established markets, where participants mainly replicate existing ventures to add supply to existing markets, often because no other employment opportunities are available. Moreover, when researchers do engage with the phenomenon, it tends to be at a macro-level stressing the institutional drivers of homogeneity. However, there is ample reason to believe this is an oversimplification of a complex phenomenon. As such, we undertook a 33-month qualitative study in Koforidua, Ghana. We found six distinct pathways through which replication occurs, and that use of these pathways is guided by entrepreneurs’ knowledge and financial constraints. More than this, we find that entrepreneuring enables the dynamic accumulation of knowledge and resources, allowing entrepreneurs to progressively relax constraints and to undertake more agentic venture selection processes.

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)0.001
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 score0.684
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0020.002
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 head0.259
Teacher spread0.220 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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