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Factors Affecting Reconsumption Intention in Herbal Medicine: Evidence from Indonesia

2024· article· en· W4403073964 on OpenAlexvenueno aff
Elizabeth ELIZABETH, Margono Margono, Fatchur Rohman, Ananda Sabil Hussein

Bibliographic record

VenueInternational Journal of Analysis and Applications · 2024
Typearticle
Languageen
FieldHealth Professions
TopicDiverse Scientific Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTraditional medicineMedicineMathematicsPsychology

Abstract

fetched live from OpenAlex

This study aims to investigate the mediating role of customer engagement on the effect of trust on reconsumption intention. This study was conducted on herbal medicine consumers in Indonesia. Using a quantitative approach involving 425 herbal consumers in Indonesia with purposive sampling techniques using criteria who are 18 years old and have consumed herbal medicine at least 2 times in the past month. Data analysis using covariance-based structural equation modelling (CB-SEM) using AMOS application. The study found that trust had no significant effect on reconsumption intention. Customer engagement has a significant effect on reconsumption intention. Trust has a significant effect on customer engagement. Then, customer engagement acts as a complete mediation on the influence of trust on reconsumption intention in herbal medicine consumers in Indonesia. These results suggest marketers need to pay attention to engagement to increase reconsumption intention using consumer trust.

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)0.002
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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.159
GPT teacher head0.500
Teacher spread0.342 · 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
Published2024
Admission routes1
Has abstractyes

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