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Record W3206037754

Product Diversification and the Financial Performance of Manufacturing Companies in Kenya

2019· article· en· W3206037754 on OpenAlexaff
Stephen Kanini, Patrick Kibati, Stella Muhanji

Bibliographic record

VenueSSRN Electronic Journal · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInsurance and Financial Risk Management
Canadian institutionsSaint Paul University
Fundersnot available
KeywordsDiversification (marketing strategy)PortfolioBusinessVolatility (finance)Return on assetsFinanceProfitability indexMarketing
DOInot available

Abstract

fetched live from OpenAlex

This study sought to establish the impact product diversification strategies as used by manufacturing entities in Kenya on the financial performance of these entities focusing on the earnings before interest and tax (EBIT) and return on assets (ROA). Limited research has been carried out on how manufacturing entities in Kenya manage operational risks despite these entities facing high volatility in the operating environment. The objectives of the study therefore focus on how product diversification as a risk management strategy influences the financial performance of manufacturing entities in Kenya. The research was based on the modern portfolio theory as by carefully choosing of investments to be included in a portfolio; an investor can effectively minimize the risk exposure and in the process maximize the portfolio expected return. The study used ten year panel data for the period spanning 2007 – 2016 from a sample of forty nine companies. From the findings, the null hypotheses of the study were not rejected implying that product diversification does not have a significant influence on the financial performance of manufacturing entities in Kenya when measured against both EBITS and ROA.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.000
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 head0.008
GPT teacher head0.178
Teacher spread0.169 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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