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Record W2806465643 · doi:10.5539/mas.v12n6p151

The Extent of Using and Trusting Capital Budgeting Methods in Projects Appraisal in Palestinian Corporations

2018· article· en· W2806465643 on OpenAlexvenueno aff
Ahmad N. H. Anabtawi

Bibliographic record

VenueModern Applied Science · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCapital Investment and Risk Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCapital budgetingNet present valuePayback periodPalestineCash flowAccountingProject appraisalBusinessValue (mathematics)Capital (architecture)FinanceEconomicsComputer scienceMicroeconomicsProduction (economics)

Abstract

fetched live from OpenAlex

This research paper aims to find the degree of the use as well as the trust of the Net Present Value (NPV), Payback Period (PBP), Internal Rate of Returns (IRR) and Accounting Rate of Returns (ARR) as a key capital budgeting method. The research conducted on the listed corporations in Palestine which are 48 company. A questionnaire distributed on 77 financial and project/operations managers in these corporations with 67 responds. The result shows that both discounted and non-discounted cash flows methods are used and trusted by Palestine public corporations. However, on the other hand, the above four methods are volatile in term of use and trust. The most used and trusted capital budgeting method is the Payback period (PBP). This followed by the Net Present Value (NPV). Accounting Rate of Retunes (ARR) becomes third. Thus the least used and trusted method is the Internal Rate of Returns (IRR).

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)0.014
metaresearch head score (Gemma)0.027
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.078
GPT teacher head0.335
Teacher spread0.257 · 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
Published2018
Admission routes1
Has abstractyes

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