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Record W4311783337 · doi:10.5267/j.ijdns.2022.11.008

The effect of social media marketing, SerQual, eWOM on purchase intention mediated by brand image and brand trust: Evidence from black sweet coffee shop

2022· article· en· W4311783337 on OpenAlexvenueno aff
Ivan Armawan, Sudarmiatin Sudarmiatin, Agus Hermawan, Wening Patmi Rahayu

Bibliographic record

VenueInternational Journal of Data and Network Science · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsnot available
FundersUniversitas Negeri Malang
KeywordsBusinessAdvertisingSocial mediaMediationBrand imageMarketingSample (material)Quality (philosophy)Brand awarenessSociologyComputer science

Abstract

fetched live from OpenAlex

The research analysis of the study was to determine the influence of Social Media Marketing, Service Quality, and eWOM on Purchase Intention (Black Sweet Coffee Shop) through Brand as mediation. The type of research is quantitative with a case study research design. The implementation of research was carried out in Indonesia, especially in the city of Balikpapan. The subject of the study was a sweet Black Café Consumer who had used sweet Black Products with a sample count of 518 using THE SPSS-SEM Amos 22. The results of this discovery show that there was a direct influence of social media marketing, SerQual, and eWOM on Purchase Intention. The theoretical implication of this study is to find additional knowledge about marketing strategies in the field of SMEs. and integrate marketing and technology capabilities to optimize social media marketing against the purchase intentions of SMEs consumers, especially coffee shop franchises.

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)0.003
metaresearch head score (Gemma)0.010
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 score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.022
GPT teacher head0.328
Teacher spread0.306 · 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

Citations31
Published2022
Admission routes1
Has abstractyes

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