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Record W2072354718 · doi:10.5539/jms.v4n3p157

Sustainable Supply Chain Management as a Strategic Tool for Competitive Advantage in Tea Industry in Kenya

2014· article· en· W2072354718 on OpenAlexvenueno aff
Barasa Peter Wamalwa

Bibliographic record

VenueJournal of Management and Sustainability · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsnot available
Fundersnot available
KeywordsKenyaCompetitive advantageBusinessSupply chainMarketingSupply chain managementPopulationOrder (exchange)Sustainable growth rateSample (material)Industrial organization

Abstract

fetched live from OpenAlex

It is assumed that companies that utilize sustainable supply chain management as a strategic tool in businessmanagement are likely to have a competitive edge over others. However, this is contrary to the Tea Industry inKenya. The main purpose of this research was to establish the role of sustainable supply chain management as astrategic tool for competitive advantage in the tea industry in Kenya. The specific objective was to find out towhat extent the supply chain collaborative strategy as a tool for competitive advantage is used by the companiesin tea industry in Kenya. The mixed research design was used in the study. The target population was the teacompanies in Kenya and the sample of eight Tea Companies were purposively selected for the study. Datacollection was done by use of both structured questionnaires and oral interview to get the primary data while thesecondary data was obtained by documentary analysis. The results finding indicated that sustainable supplychain management as a strategic tool contributes to the competitive advantage of Kenyan tea companies in theglobal market. The results provide information to the tea companies to come up with sustainable strategies intheir supply chain management in order ensure the Kenyan tea remains competitive in the global market.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.001
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 head0.010
GPT teacher head0.253
Teacher spread0.243 · 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 designTheoretical or conceptual
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

Citations17
Published2014
Admission routes1
Has abstractyes

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