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Analisa Bauran Pemasaran Terhadap Volume Penjualan (Studi Kasus Toko Pakaian Boysdontcry)

2023· article· en· W4400001489 on OpenAlexaff
Raden purwa galuh Wijaya, Sutardjo, Elly Setiadewi

Bibliographic record

VenueJurnal Teknologika · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsRegression analysisStatisticsVolume (thermodynamics)MarketingValue (mathematics)Marketing mix modelingStatistical analysisVariablesComputer scienceMarketing mixMathematicsBusinessReturn on marketing investmentMarketing effectiveness

Abstract

fetched live from OpenAlex

Boysdontcry is a business engaged in fashion distros. Sales volume at Boysdontcry has decreased and increased, so researchers analyze whether the strategy implemented by Boysdontcry is right. Data analysis in this study uses quantitative analysis methods, namely by managing data presented in the form of frequency and percentage tables, in this study using simple regression analysis methods using statistical application software tools SPSS (Statistical Program for Social Science)). The validity test is used to measure the appropriateness or absence of a questionnaire and produce a valid value, namely the r value of the table r count. The overall reliability test of the question items from each variable can be used and can be distributed to all respondents, because each item shows valid and reliable results based on the results of constant value data of 1.394. Stating that the regression coefficient of 0.204 means that the marketing mix has a positive relationship with sales volume. Every 1 unit increase in the marketing marketing mix will affect the average sales volume increase of 0.204. Conversely, every decrease in sales volume will affect the average decrease in marketing mix by 0.204. Boysdontcry is a business engaged in fashion distros. Sales volume at Boysdontcry has decreased and increased, so researchers analyze whether the strategy implemented by Boysdontcry is right. Data analysis in this study uses quantitative analysis methods, namely by managing data presented in the form of frequency and percentage tables, in this study using simple regression analysis methods using statistical application software tools SPSS (Statistical Program for Social Science)). The validity test is used to measure the appropriateness or absence of a questionnaire and produce a valid value, namely the r value of the table r count. The overall reliability test of the question items from each variable can be used and can be distributed to all respondents, because each item shows valid and reliable results based on the results of constant value data of 1.394. Stating that the regression coefficient of 0.204 means that the marketing mix has a positive relationship with sales volume. Every 1 unit increase in the marketing marketing mix will affect the average sales volume increase of 0.204. Conversely, every decrease in sales volume will affect the average decrease in marketing mix by 0.204.

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)0.001
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 score0.290
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.045
GPT teacher head0.311
Teacher spread0.265 · 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
Published2023
Admission routes1
Has abstractyes

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