Customer satisfaction and trust interaction model
Bibliographic record
Abstract
The purpose of this study is to identify and analyze the interaction between customer satisfaction and trust in a structural model. Sample consists of 210 Suzuki motor bikes’ users, aged 17-64, domiciled in Medan. Structural equation model approach is used to process raw data. Respondents' opinions were taken proportionally throughout the Medan city sub-districts. Questionnaires were submitted accidentally. The results show that customer satisfaction and trust partially had positive and significant effect on brand equity and repurchase intention. Brand equity did not show any indirect causal relationship mediator of the three exogenous variables to repurchase intention. Programs related to customer satisfaction and trust better increase brand equity and repurchase intention directly. Interaction variable existence in research model increases Chi-Square probability, decreases standard deviation, and increases t-value. Interaction variable creates synergy on the influence of customer satisfaction and trust to increase brand trust. Any programs to increase brand equity through customer satisfaction and trust should be implemented, simultaneously. The two exogenous variables will bring out the synergy that comes from this interaction.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".