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Record W2767382678 · doi:10.1002/sej.1281

Accelerator expertise: <scp>U</scp> nderstanding the intermediary role of accelerators in the development of the <scp>B</scp> angalore entrepreneurial ecosystem

2017· article· en· W2767382678 on OpenAlexaff
Ketan Goswami, Janet Mitchell, Suresh Bhagavatula

Bibliographic record

VenueStrategic Entrepreneurship Journal · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsWestern University
Fundersnot available
KeywordsBusinessEcosystemSituatedAdditionalityEntrepreneurshipMarketingKnowledge managementComputer scienceEnvironmental economicsEcologyEconomicsFinance

Abstract

fetched live from OpenAlex

Research Summary : To understand the intermediary role of accelerators in the developing regional entrepreneurial ecosystem of Bangalore, we analyze data from 54 interviews with accelerator graduates, accelerator managers, and other ecosystem stakeholders and from 49 websites, 13 online video interviews, 26 online news sources, and 301 pages of policy documents. Specifically, we adopt a socially situated entrepreneurial cognition approach to theorize how accelerator expertise, existing at a meso‐level, intermediates between (micro‐level) founders and the (macro‐level) ecosystem. In our model, four types of accelerator expertise—connection, development, coordination, and selection—together increase stakeholders’ commitment to the entrepreneurial ecosystem, leading to venture validation (success or failure) and ecosystem additionality. These findings indicate that accelerators contribute to ecosystems in a way that is distinct from, but supportive of, building individual ventures. Managerial Summary : Accelerators are a new form of entrepreneurial support organization. These organizations typically focus on developing individual start‐ups, but we find that they also help develop entrepreneurial ecosystems. They do so by acting as a bridge between start‐ups and the broader entrepreneurial environmental resources by: (a) helping form connections, (b) helping develop individual start‐ups, (c) helping coordinate the right match among the various players in the ecosystem, and (d) helping select mentors and founders with the appropriate motivation and knowledge. As these accelerators apply this expertise in this go‐between role, they help build commitment to the broader ecosystem. Furthermore, they enable success (or fast failure) of individual start‐ups and do so in a way that develops the overall entrepreneurial capacity of the broader entrepreneurial ecosystem.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.050
GPT teacher head0.259
Teacher spread0.208 · 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

Citations293
Published2017
Admission routes1
Has abstractyes

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