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Record W4409824204 · doi:10.17977/um042v28i2p1-7

The Influence of Viral Marketing, Flash Sales, and FOMO on Impulsive Buying of Skintific Products

2023· article· en· W4409824204 on OpenAlexaboutno aff
Sri Wahyuni, Amelindha Vania

Bibliographic record

VenueEkonomi Bisnis · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessAdvertisingMarketingViral marketingFlash (photography)Computer scienceArtSocial media

Abstract

fetched live from OpenAlex

Skintific is a beauty product from Canada that has introduced its products since 2021. This beauty brand offers effective solutions to strengthen the skin barrier and overcome various skin problems through its superior products. This research aims to determine the influence of Viral Marketing, Flash Sales, and FOMO on impulse purchases of Scientific products. The population of this study is GenZ on the island of Java. The sampling technique used is purposive sampling, which is a technique that gives researchers the freedom to select samples based on certain criteria. The sample collected amounted to 92 respondents. The method used is quantitative research with descriptive statistical analysis methods and processed using PLS-SEM 4. The results of this research show that viral marketing has an insignificant positive influence on impulse purchases, Flash Sale has an insignificant positive influence on impulse purchases, and FOMO has a significant positive influence on impulse purchasing of Scientific products.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.014
GPT teacher head0.229
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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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