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Record W3216369194 · doi:10.18280/ijsdp.160616

Roles of Regulation and Lifestyle on Indonesian Coffee Consumption Behavior Across Generations

2021· article· en· W3216369194 on OpenAlexvenueno aff
Adi Prasetya Utama, Ujang Sumarwan, Arif Imam Suroso, Mukhamad Najib

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsnot available
Fundersnot available
KeywordsRespondentIndonesianConsumption (sociology)Consumer behaviourAction (physics)PsychologyMarketingAdvertisingSocial psychologyBusinessSociologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Coffee beverage consumption has become more important in the recent years and has touched the lifestyles of the old and young people. The research of coffee consumption, especially in relation to the lifestyles of the people, therefore needs to be intensified. The purpose of this study was to compare the contribution of regulation and lifestyle in determining coffee drink consumption among old and young consumers. The theory adopted was the SOR (stimulus-organism-response) theory and the AISAS (attention-interest-search-action-share) model approach. Data were collected online in April-May 2020, and obtained 413 valid respondent data, which consist of 207 old respondents and 206 young respondents. The results showed that the lifestyle variable had positive effects on attitude and search behavior of both young and old generations. The lifestyle variable also had significant effects on coffee consumption for both generations. The regulation variable had positive effects on attitude and action of young coffee consumers, but it had no effect on attitude, search and action of old consumers. The research suggests that, to increase coffee consumption, better regulation approach should consider the age of the consumer. Especially, more attention should be paid for the regulation on coffee marketing for older generation.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.112
Threshold uncertainty score0.212

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.024
GPT teacher head0.323
Teacher spread0.300 · 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

Citations11
Published2021
Admission routes1
Has abstractyes

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