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Record W2167580333 · doi:10.1109/picmet.2007.4349456

Benchmarking the Turkish Business Incubators: Supporting Innovation through Innovative Infrastructures

2007· article· en· W2167580333 on OpenAlexaboutno aff
Dilek Çetindamar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsBenchmarkingIncubatorTurkishGeneral partnershipBusinessSustainabilityCorporate governanceProcess managementIndustrial organizationMarketingFinance

Abstract

fetched live from OpenAlex

As business incubators (Bl) are one of the infrastructures used to promote and support entrepreneurs throughout the world in order to support the realization of innovative ideas, this paper will present a benchmark study of the Turkish business incubators to understand the existing infrastructure and to develop policy suggestions for the improvement of this infrastructure. Even though the main focus of the paper will be the Turkish Bl, the comparison will be based on two international studies. One of them is an international study of 12 selected countries (Armenia, Canada, Croatia, Hungary, Norway, Poland, Ukraine, Republic of Moldova, Republic of Serbia, Romania, Slovakia and Slovenia). The second study is carried out in 269 Bl across European member countries and around 2000 companies. Seven key factors seem to be used as indicators for benchmarking Bis: 1) the role of stakeholders, 2) locational and physical aspects of incubator operations, 3) the definition of the incubator's 'mission', 4) the tenants companies they attract as clients, 5) issues relating to the financing of incubator start up and operating cost, 6) governance, and 7) sustainability of Bl. The empirical part introduces the Turkish case by presenting the survey results of 11 business incubators, representing 27% of business incubators operating in Turkey. Based on the benchmarking analysis, three main results can be driven for policy makers. First, Turkish Bis have higher share of partnership structure as well as private Bis but business involvement seems restricted to being a formal partner, except for the business-owned Bis. Second, firms in Bis focus in manufacturing related technologies, demanding more involvement of universities in order to change firms orientation into technology. Third, Turkish Bis need to establish more professional management. Privately-owned Bis in Turkey employ professionals, while government-owned Bis in Turkey are run with appointed managers.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.269
Teacher spread0.251 · 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 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

Citations2
Published2007
Admission routes1
Has abstractyes

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