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Record W2990588640 · doi:10.5539/ibr.v13n1p64

Anecdotal Evidence of the Role of Incubation in the Growth of Business Start-Ups in Uganda

2019· article· en· W2990588640 on OpenAlexvenueno aff
Anthony Tibaingana

Bibliographic record

VenueInternational Business Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsnot available
FundersStyrelsen för Internationellt Utvecklingssamarbete
KeywordsMentorshipBusinessMarketingCoachingStart upQualitative researchFocus groupEntrepreneurshipBusiness administrationPublic relationsManagementEconomicsPolitical scienceFinanceSociology

Abstract

fetched live from OpenAlex

Although incubation is a well-known and accepted strategy for the growth of business start-ups in Uganda, little is known about its extent. What is known is that the majority of the start-ups fail in less than one year. This study explored the role of incubators on the growth of business start-ups in Uganda. The study interviewed managers of the incubation services and business starts-ups on how they received this support. The study is qualitative and respondents were purposively selected. Key informant interviews and focus group discussions were used to capture the perceptions of the services and how the support enabled the enterprises to grow. The findings show that various services were offered to support the growth of business start-ups. The services ranged from the creation of networks to other business development services such as mentorship, coaching, and marketing. The perceptions of the owners of business start-ups were somewhat mixed because while the majority viewed the support as crucial in the growth of their start-ups, a few others said they did not. Thus, a few owners of business start-up viewed incubators as playing a limited role. The findings are pertinent for policy formulation on the role of business start-ups and for streamlining incubation support processes in emerging economies.

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)0.002
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 score0.028
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.050
GPT teacher head0.319
Teacher spread0.269 · 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
Published2019
Admission routes1
Has abstractyes

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