Examining the role of product quality in enhancing brand trust and purchase decisions: Evidence from smartphone users in Central Jakarta
Bibliographic record
Abstract
Based on Counterpoint Research data, from quarter 1 of 2022 to quarter 1 of 2023, Xiaomi is the cellphone vendor that has fallen the most, with a decline in market share of 24.8 percent YoY. This study examines the effect of product quality on brand trust and its impact on purchasing decisions for Xiaomi smartphone products in Central Jakarta. The research method used was a survey with data collected through a questionnaire distributed to 170 Xiaomi smartphone users in Central Jakarta. Data analysis was performed using statistical methods, including validity, reliability, and Structural Equation Modeling (SEM) tests with SmartPLS 4.0. The results show that product quality significantly increased brand trust, which had a positive impact on purchasing decisions. These findings suggest that improving product quality can increase consumer trust in the brand, which, in turn, drives higher purchase decisions. This study has several limitations, including focusing only on Xiaomi users in Central Jakarta and not considering other external factors that might influence purchasing decisions. This research helps companies understand how product quality is critical to building brand trust, and how consumers’ purchasing decisions are influenced. These results can be used as a basis for creating better marketing strategies and increasing consumer satisfaction.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".