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Record W3130720202 · doi:10.29103/e-mabis.v21i2.500

EFEKTIITAS MEDIA SOSIAL DAN ANGGARAN BIAYA TERHADAP KINERJA BISNIS DI PERUSAHAAN DROPSHIP DI WILAYAH SURAKARTA DENGAN PEMASARAN BERBASIS OUTPUT SEBAGAI VARIABEL INTERVENING (Studi Perusahaan Dropship di Wilayah Surakarta)

2020· article· id· W3130720202 on OpenAlexaff
Aris Haryanto, Septiana Novita Dewi

Bibliographic record

VenueE-Mabis/E-Mabis: Jurnal Ekonomi Manajemen dan Bisnis · 2020
Typearticle
Languageid
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsAdidas (Canada)
Fundersnot available
KeywordsBusiness administrationBusiness

Abstract

fetched live from OpenAlex

Penelitian ini bertujuan mengetahui secara empiris dan menganalisis pengaruh media sosial dan anggaran biaya terhadap pemasaran berbasis output Perusahaan Dropship di wilayah Surakarta. Mengetahui secara empiris dan menganalisis pengaruh media sosial, anggaran biaya dan pemasaran berbasis output terhadap kinerja bisnis Perusahaan Dropship di wilayah Surakarta. Dalam penelitan ini yang menjadi populasi adalah Populasi dalam penelitian ini adalah Perusahaan Dropship di wilayah Surakarta yang tergabung pada komunitas toko online sejumlah 40 perusahaan. Jumlah sampel dalam penelitian ini berjumlah 40 responden. Teknik pengambilan sampel dengan menggunakan sampling jenuh atau sensus yaitu semua anggota populasi digunakan sebagai sampel. Hasil kesimpulan dalam penelitian ini adalah Media sosial berpengaruh positif dan signifikan terhadap pemasaran berbasis output. Anggaran biaya berpengaruh positif dan signifikan terhadap pemasaran berbasis output. Media sosial berpengaruh negatif dan tidak signifikan terhadap kinerja. Anggaran biaya berpengaruh positif dan signifikan terhadap kinerja. Pemasaran berbasis output berpengaruh positif dan signifikan terhadap kinerja. Dari analisis jalur diketahui jalur langsung anggaran biaya berpengaruh terhadap kinerja, merupakan jalur yang dominan atau efektif untuk meningkatkan kinerja

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)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0040.005
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0010.004
Science and technology studies0.0050.003
Scholarly communication0.0050.004
Open science0.0060.003
Research integrity0.0020.004
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 head0.042
GPT teacher head0.261
Teacher spread0.219 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2020
Admission routes1
Has abstractyes

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