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Record W3020558390 · doi:10.5539/mas.v14n5p19

The Influence of the Trade-off between Profitability and Future Increases in Sales on Cost Stickiness: Evidence from Jordan

2020· article· en· W3020558390 on OpenAlexvenueno aff
Mohammad Murdi Alenezi

Bibliographic record

VenueModern Applied Science · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsProfitability indexStock exchangeBusinessSample (material)Regression analysisPopulationInvestment (military)MarketingFinance

Abstract

fetched live from OpenAlex

This study aims at analyzing cost stickiness under the dilemma between current profitability and future sales increase. The study population consisted of all Jordanian industrial companies listed on the Amman Stock Exchange (ASE) during the period (2007-2017). The study sample consisted of (30) industrial companies, were used in the analysis. Panel data regression was used to test the relationship between the variables in the study. Results supported the Anderson et al. (2003) argument in that selling, general and administrative expense for Jordanian industrial firms listed in Amman stock exchange (ASE) follow the sticky cost behaviour, they increased by (0.34%) for 1% increase in sales, however, they didn’t change by any sales decrease. During sales decline results showed that future sales growth did not have a stressing effect on cost stickiness and didn’t drive greater cost stickiness, however, changes in profitability was proved to have a significant positive relation to cost stickiness when sales decrease, meaning that managers apply greater adjustments in SGA (greater cost stickiness) in the case of the attainment of unfavourable changes in profitability. The study recommended a number of recommendations, including Companies should know the factors that affect the cost behavior and take into consideration when analyzing costs and making administrative decisions in companies which will, in turn, improve the process of making administrative decisions and investment decisions.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.181
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.018
GPT teacher head0.226
Teacher spread0.209 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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