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Record W4414485017 · doi:10.33423/jabe.v27i5.7856

Influence of Financial Flexibility on Performance of State-Owned Sugar Manufacturing Corporation Projects in Western Kenya

2025· article· en· W4414485017 on OpenAlexvenueno aff
Mathew Elijah Kawour, Charles M. Rambo, Paul A. Odundo

Bibliographic record

VenueJournal of Applied Business and Economics · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic-Private Partnership Projects
Canadian institutionsnot available
Fundersnot available
KeywordsCorporationFlexibility (engineering)Sample (material)RevenueGovernment (linguistics)Order (exchange)PopulationAgency (philosophy)Capital structure

Abstract

fetched live from OpenAlex

This study examined the influence of financial flexibility on performance of State sugar manufacturing corporations in Kenya. The study was guided by Modigliani and Miller’s capital structure model mainly (trade-off, pecking order and agency cost) theories, and was based on pragmatic paradigm which provides for the use of both qualitative and quantitative research methodologies. It targeted a population of 1,145 people, drawn from employees of State sugar corporations, and used a sample size of 291, obtained from Krejcie and Morgan's (1970) Table. A structured questionnaire and interview guide were used to collect data. SPSS version 25 was used to analyse data and Hypothesis was tested at a=0.05 significance level. Pearson’s correlation and linear regression analysis showed a positive correlation between the variables (p = 0.000 < 0.05), implying that financial flexibility significantly influences the performance of State sugar firms. It was recommended that, to enhance financial flexibility in State sugar corporations, the Government should focus on strategies that help reduce costs, improve revenue generation, and strengthen financial management practices.

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)0.000
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 score0.358
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.020
GPT teacher head0.224
Teacher spread0.205 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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