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Record W4398255646 · doi:10.5430/afr.v13n2p142

Working Capital Management and Financial Performance: Evidence from Nigeria’s Public Listed Manufacturing Companies

2024· article· en· W4398255646 on OpenAlexvenueno aff
Ibrahim Shuayb Kademi, Norhayati Alias, Noor Hasniza Haron, Raziah Bi Mohamed Sadique

Bibliographic record

VenueAccounting and Finance Research · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWorking Capital and Financial Performance
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessFinanceWorking capitalCapital (architecture)Financial managementAccounting

Abstract

fetched live from OpenAlex

Working capital management is important in corporate finance and considerably impacts organizational profitability. The study investigates how working capital management impacts the performance (specifically in the context of financials) of publicly traded manufacturing companies listed on the Nigeria Stock Exchange (NSE). Data collected for this study was analyzed using Statistical Package for Social Sciences (SPSS) based on the sample of 18 manufacturing companies for five years from 2013 to 2017. Working capital management is important for all organizations as it can considerably impact profitability and financial performance. The linear regression study revealed a notable positive correlation between the Account Payable Period and profitability. An escalation in the duration of accounts payable is associated with increased profitability. The second regression model conclusion indicates that a reduction in the duration (days) to collect sales from consumers is linked to a reduction in the profit since accounts receivable are negatively and insignificantly correlated with profitability. The third discovery in this study revealed an insignificant adverse relationship between the conversion period for inventory and profitability. Manufacturing companies need to turn their inventory into sales to increase profits promptly. This analysis revealed an insignificant inverse correlation between the conversion cycle for cash and profitability.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.803
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0000.001
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 head0.057
GPT teacher head0.274
Teacher spread0.217 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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