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Record W4385973795 · doi:10.5267/j.uscm.2023.7.005

Exploratory analysis of natural cosmetic products purchase intention: Evidence from Jakarta, Indonesia

2023· article· en· W4385973795 on OpenAlexvenueno aff
Kilala Tilaar, Asep Mulyana, Rita Komaladewi, Kurniawan Saefullah

Bibliographic record

VenueUncertain Supply Chain Management · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsnot available
Fundersnot available
KeywordsStorytellingStructural equation modelingBusinessExploratory researchProduct (mathematics)MarketingAdvertisingConsumption (sociology)JudgementSociologyMathematicsNarrativeStatistics

Abstract

fetched live from OpenAlex

In the COVID-19 era, green consumption has risen into a global trend, leading beauty products to be more environmentally friendly to satisfy these new consumers’ needs. However, not every natural beauty brand, especially natural cosmetic products, survived in the market. This study aimed to examine the cause and effect of a phenomenon of storytelling marketing strategies on the public's purchase intention for cosmetic products made from natural ingredients. The literature study showed that the relationship between storytelling and purchase intention is somewhat inconsistent. As a countermeasure and study gap, this study implemented product innovation as a mediating variable. This research was carried out quantitatively in Jakarta, Indonesia. Data used in this study was primarily obtained through questionnaires in 2021 and judgement sampling of 200 respondents. The data were tested using Structural Equation Model Partial Least Square (SEM-PLS) models’ technique on SmartPLS 3.0. The results showed that storytelling positively increases natural cosmetic purchase intention through product innovation.

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 score0.117
Threshold uncertainty score0.788

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.041
GPT teacher head0.294
Teacher spread0.254 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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