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Record W2022804566 · doi:10.5539/ijef.v6n6p234

Cost Management Practices and Firm’s Performance of Manufacturing Organizations

2014· article· en· W2022804566 on OpenAlexvenueno aff
Oyerogba Ezekiel Oluwagbemiga, Olaleye Michael Olugbenga, Solomon Adeoluwa Zaccheaus

Bibliographic record

VenueInternational Journal of Economics and Finance · 2014
Typearticle
Languageen
FieldDecision Sciences
TopicOperations Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsProfitability indexCost centreBusinessVariable costStock exchangeTotal absorption costingProfit (economics)Profit maximizationIndustrial organizationCost accountingOperations managementOverhead (engineering)AuditEconomicsFinanceAccountingMicroeconomicsComputer science

Abstract

fetched live from OpenAlex

This study investigates the relationship that exists between cost management practices and firm’s performance in the manufacturing organizations using data from 40 manufacturing companies listed on the Nigeria stock exchange during the period of 2003 to 2012. Four hypotheses were formulated for the study and tested using t-statistic. The study relied on secondary data extracted from the audited financial statement of the selected companies. Direct material cost, direct labour cost, production overhead cost and administrative overhead cost were taken as independent cost management variables while profitability (Operating profit) was taken as dependent variable representing the firm’s performance. The result indicates that a positive significant relationship exists between cost management practices and firm’s performance in the manufacturing organization. It is therefore recommended that a cost reduction strategy with emphasis on production overhead cost and administrative overhead cost should be embarked upon if their profit maximization and wealth creation objective must be achieved.

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)0.006
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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.049
GPT teacher head0.334
Teacher spread0.285 · 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

Citations37
Published2014
Admission routes1
Has abstractyes

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