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Record W3179360130

Evaluation of the Nexus Between Revenue Volatility From Commodity Sales and Financial Performance of Manufacturing Companies in Kenya

2020· article· en· W3179360130 on OpenAlex
Stephen Kanini, Patrick Kibati, Stella Muhanji

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSSRN Electronic Journal · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Reporting and Valuation Research
Canadian institutionsSaint Paul University
Fundersnot available
KeywordsVolatility (finance)RevenueBusinessNexus (standard)EarningsPanel dataFinanceEconomicsEconometrics
DOInot available

Abstract

fetched live from OpenAlex

The researchers sought to evaluate the impact of revenue volatility on the financial performance of the manufacturing companies in Kenya given the anecdotal arguments that point to both positive and negative relationships. Revenue volatility was measured using the coefficient of variation of sales while financial performance was measured using earnings before interest and tax (EBIT) and return on assets (ROA). The data was analyzed using long run and dynamic panel data models and appropriate specification tests used. The researchers came to the conclusion that revenue volatility has a negative and significant impact on the financial performance and should be of particular concern for manufacturing entities in Kenya.

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.

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)0.005
metaresearch head score (Gemma)0.001
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 score0.048
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.059
GPT teacher head0.287
Teacher spread0.228 · 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