Purchase intention of Muslim consumers on TikTok live stream: Assessing the role of trust, reliability, and TikTok marketing activitie
Bibliographic record
Abstract
This study explores the impact of TikTok marketing activities in stimulating consumer purchasing intention with trust and reliability during live streaming sessions for Muslim apparel. While there is a growing interest in shopping through live streaming, little study has been done regarding Islamic marketing. To achieve the purpose of the study, a convenience sampling method was adopted to collect data from 225 participants for assessing the effects of TikTok marketing activities on consumer behaviour. The findings suggest that most of the TikTok marketing activities enhance trust and credibility, hence influencing positive intentions to buy among consumers while attending a live stream. The insights also apply to three broader fields of Islamic marketing, consumer behaviour, and sustainable marketing, while again supporting the United Nations SDGs specifically Goal 12: Responsible Consumption and Production. This paper therefore advocates for more sustainable consumption patterns and responsible marketing practices, as it gives confidence and trust in online shopping for ethical consumption of goods in digital commerce. Beyond this, the study highlights how social commerce can be instrumental for the realization of economic growth (Goal 8), innovation within marketing practices, and inclusive and sustainable economic participation via digital platforms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".