The Effect of Sustainable Brand Equity on Customer Satisfaction and Customer Loyalty Using Customer Trust as Mediation Variable
Bibliographic record
Abstract
The primary goal of this study is to experimentally examine the mediating role of customer trust in the impact of sustainable brand equity on customer satisfaction and customer loyalty.We employed a quantitative methodology and designed a survey questionnaire to measure sustainable brand equity.The survey was administered to 220 construction material retailers in Jakarta, Indonesia.Our investigation focuses on how antecedents of sustainable brand equity influence customer perceptions of satisfaction and loyalty in the retail trade for building materials.We used structural equation modeling (SEM) in AMOS version 26 for the analysis.Our findings demonstrate that antecedents of sustainable brand equity (such as brand awareness, brand identification, physical quality, staff behavior, lifestyle congruence, and ideal self-congruence) directly and significantly influence customer trust.In turn, customer trust directly and significantly influences customer satisfaction and loyalty.Interestingly, while customer trust does not impact customer loyalty, customer satisfaction does, with a strong influence.Hence, customer loyalty is greatly affected by customer satisfaction.This study's significance lies in demonstrating how the development of sustainable brand equity affects trust, satisfaction, and loyalty among building material retailers.The emergence of customer trust as a mediator between sustainable brand equity, customer satisfaction, and customer loyalty forms the focal point of this study.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".