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Record W2122375002 · doi:10.5539/ass.v8n3p25

Tales of the Survivors: The Bumiputera Entrepreneurs’ Experience

2012· article· en· W2122375002 on OpenAlexvenueno aff
Suraiya Ishak, Ahmad Raflis Che Omar, Azhar Ahmad

Bibliographic record

VenueAsian Social Science · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsMalayTurning pointPoint (geometry)MarketingBusinessEntrepreneurshipPeriod (music)

Abstract

fetched live from OpenAlex

Survival in the highly precarious business environment demands credible and resilient entrepreneurs. The issue evolves from merely “how to get started” towards “how to reach the highest potential throughout the entire business life cycle”. It is expected that entrepreneurs encounter several turbulences throughout their business cycles before stabilizing within the growth range. It is also posited that each cycle reflects different kind of challenges and barriers, thus leading to different moves and strategies. The ability to react accurately to those challenges will provide different business outcomes. This article aims to describe and reveal the experience of Malay (also known as Bumiputera or son of the soil) entrepreneurs pertaining to their business endeavours. This study adopted a case-study technique with four Malay entrepreneurs through in-depth interviews. This study concluded that all respondents encountered a period of turbulence before making a move to another stage of business cycle. To initiate the turning point, these entrepreneurs introduced some changes either in their products or job processes. In addition, the respondents noted that entrepreneurs without some working or industrial knowledge took relatively longer periods to reach their turning point. Experience entrepreneurs would reach the turning point earlier due to the benefits gained from their past business network as well as the enrichment of tactical and managerial tacit skills that they had acquired.

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 score0.129
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
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 head0.020
GPT teacher head0.255
Teacher spread0.235 · 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

Citations14
Published2012
Admission routes1
Has abstractyes

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