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Record W2594405651 · doi:10.5539/ijef.v9n4p63

The Influence of Economic Value Added and Return on Assets on Created Shareholders Value: A Comparative Study in Jordanian Public Industrial Firms

2017· article· en· W2594405651 on OpenAlexvenueno aff
Sliman S. Alsoboa

Bibliographic record

VenueInternational Journal of Economics and Finance · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Reporting and Valuation Research
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic Value AddedReturn on assetsShareholderValue (mathematics)Market value addedMeasure (data warehouse)Market valueEconomicsBusinessEconometricsShareholder valueActuarial scienceFinanceMathematicsStatisticsMicroeconomicsComputer scienceProfitability index

Abstract

fetched live from OpenAlex

This study has two main objectives. The first one is to address the relationship between Economic Value Added (EVA) and Created Shareholders Value (CSV) in Jordanian public industrial firms (JPIF), comparing to the Return on Assets (ROA) over the period 2011-2015. The second objective is to address the possible superiority of EVA to ROA by explaining the changes in CSV for JPIF. In this study, CSV is measured using two models; Fernandez model and market value added model. Multiple and simple regressions were used in the study. These analyses have shown, generally, that the superiority of EVA in predicting and evaluating the CSV could be put into a conclusive and positive light compared to ROA. However, the results suggested that one financial measure cannot be enough to measure neither CSV nor firms’ performance. Therefore, this study highly recommends that JPIF use a combination of different measure in assessing and evaluating their value and performance, especially modern indicators.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.135
GPT teacher head0.352
Teacher spread0.216 · 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

Citations16
Published2017
Admission routes1
Has abstractyes

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