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Record W1884463739 · doi:10.5430/ijfr.v6n4p114

Role of Financial Banks in Promoting the Entrepreneurship: A Mixed Methodology Approach from Kingdom of Saudi Arabia

2015· article· en· W1884463739 on OpenAlexvenueno aff
Abdullah Mohammed Aldakhil, Muhammad Moinuddin Qazi Abro, Muhammad Adnan Khurshid, Alamzeb Aamir

Bibliographic record

VenueInternational Journal of Financial Research · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicDigital Platforms and Economics
Canadian institutionsnot available
FundersKing Saud University
KeywordsLoanUnavailabilityEntrepreneurshipBusinessQuality (philosophy)FinanceDocumentationConstruct (python library)BureaucracyQualitative researchSmall businessSoft loanAccountingMarketingNon-performing loanSociology

Abstract

fetched live from OpenAlex

The issue of getting finances for the small businesses and entrepreneurs is always been in debate and remain unresolved in many countries due to unavailability of qualified venture capitalists. The developing and emerging economies set the micro finance banks for this purpose, however, it is argued that the owner and entrepreneur faces many problems like collaterals, documentation, etc. This research focuses on the role of financial banks in promoting the small business and entrepreneurial culture in the Saudi Arabia in providing credit. The research applied a mixed methodology and at the first stage, qualitative data is collected and then the results of these structured interviews were used to construct a survey questionnaire for the quantitative analysis. The result of study shows that the levels of business cooperation and information sharing and quality of business have an important significance on the success of loan application. Furthermore, the results also support that the bureaucracy of bank in terms of loan documents requirement and loan evaluation procedure can make small business hesitate when applying for loans.

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)0.004
metaresearch head score (Gemma)0.007
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 score0.642
Threshold uncertainty score0.861

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.169
GPT teacher head0.343
Teacher spread0.173 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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