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Record W4405240194 · doi:10.52155/ijpsat.v46.1.6489

Marketing Development Strategy Of Milkfish Satay Business Using Analytical Hierarchy Process Method

2024· article· en· W4405240194 on OpenAlexaff
Muhamad Taqi, Tubagus Ismail, Meutia Meutia, Sabaruddinsah Sabaruddinsah, Dharmendra Dharmendra, Nawang Kalbuana

Bibliographic record

VenueInternational Journal of Progressive Sciences and Technologies · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFisheries and Aquaculture Studies
Canadian institutionsSmiths Detection (Canada)
Fundersnot available
KeywordsMilkfishAnalytic hierarchy processProcess (computing)Process managementHierarchyBusinessComputer scienceMarketingMathematicsFisheryOperations researchEconomicsFish <Actinopterygii>Aquaculture

Abstract

fetched live from OpenAlex

Marketing strategy is one of the spearheads of a healthy business. In practice marketing strategy faces a complicated problem since generally a good and standard strategy is only experienced by a business that has been running long and remains healthy so that it continues to generate profits. It is interesting to discuss exclusive food businesses (local products) that have a weakness of quick expiration date of only 1 day in delivery and 3 days in an open room (not in the packaging). It was found 4 P marketing strategy (Product, Price, Place &amp; Promotion) so that the Analytical Hierarchy Process model was made. The results of the study showed that the top priority level for marketing in the product category was quality such as taste that was always the same and delicious. In addition, the other three criteria namely price, place, and promotion were at a non-negotiable normal price to give an exclusive impression, added facilities to provide a comfortable impression for consumers and maintain good public relations, for example, the strong relationship about satay milkfish product through word of mouth, the mutualism symbiosis relationship between local and national TV channels. The interesting thing in the choice of marketing strategies was offline marketing rather than online marketing.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.978
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.040
GPT teacher head0.346
Teacher spread0.307 · 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 designOther design
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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