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Investigating online purchase intention of Gen Z on TikTok live stream shopping

2024· article· en· W4411707772 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHo Chi Minh City Open University Journal of Science- Economics and Business Administration · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Topics in Contemporary Research
Canadian institutionsCrosslight Software (Canada)
Fundersnot available
KeywordsBusinessAdvertisingInternet privacyLive streamingMarketingComputer scienceMultimedia

Abstract

fetched live from OpenAlex

The aim of this research is to investigate the potential determinants of online purchase intention on livestream shopping via TikTok by application of the Information System (IS) success model and the Uses and Gratification Theory (UGT). These theories have strongly proved to be effective in predicting human behavior from a social psychology standpoint, especially in explaining the motivation of consumers to have purchase intentions in online shopping contexts. Data were collected from 203 online and offline survey participants of Gen Z in the North of Vietnam. Regression techniques through SPSS 20 software were used to test the study hypotheses. The findings reveal that system quality, information quality, streamer attractiveness, para-social interaction, and price promotion positively influence online purchase intentions. Which price promotion has the most significant and positive impact on the online purchase intention of Gen Z consumers on TikTok’s livestream shopping. These results provide a more comprehensive understanding of online purchase intentions. The findings and conclusion address notable theoretical and practical implications.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.728
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
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 head0.121
GPT teacher head0.352
Teacher spread0.231 · 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