Non recursive model of consumer satisfaction and trust
Bibliographic record
Abstract
This study was designed to identify and analyze the effect of perceived value, differentiation, and emotional branding on customer satisfaction and trust; non-recursive influence between customer satisfaction and customer trust of the vivo smartphone in Medan. Respondents consist of adults who use vivo smartphones domiciled in Medan. The analysis technique uses a structural equation model with the robust maximum likelihood method. Data processing is assisted by Lisrel software and the results indicate in case the direct effect coefficient of the same latent variable is positive in the non-recursive and recursive models, the coefficient will be higher in the recursive model. Therefore, it is important for vivo smartphone management to verify first, whether the relationship between customer satisfaction and trust is reciprocal. In case it is one way, where satisfaction affects customer trust, then the strategy or program of increasing perceived value and differentiation directly at customer satisfaction is more optimal. Furthermore, it will have an impact on customer trust since the satisfaction mediating effect is higher than the customer trust mediating effect in the recursive model. The program of increasing consumer trust with emotional branding through customer satisfaction is good, since the mediation of satisfaction is significant in both non-recursive and recursive contexts.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.007 | 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".