The Extent of Using Capital Budgeting Techniques in Evaluating Manager’s Investments Projects Decisions (A Case Study on Jordanian Industrial Companies)
Bibliographic record
Abstract
The aim of this research paper is to study the extent of using capital budgeting techniques on choosing the suitable project for investment. The current research study focused on capital budgeting techniques such as Net Present Value NPV, and Internal Rate of Return IRR, and Pay Back period PB, which is considered the main tools in the hands of decision makers in deciding the best possible alternative of investment. In order to achieve the purposes of the study a questionnaire have been created (based on Graham and Harvey survey in 2001), the aim was to cover most of the Jordanian industrial companies despite of their size and ownership in the current year 2017. Resolution data were analyzed using the statistical program SSPS. Finally, the study concluded that, 58% of Jordanian industrial companies use the Net Present Value, 22% use the Payback Period, 12% use the Internal Rate of Return, and the remaining used a combination of the Accounting Rate of Return, Profitability Index, and sensitivity analysis. The current research study is expected to assess management in choosing the best capital budgeting technique in the evaluation of its future investment projects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".