Corporate image and service quality: Evidence from Indonesia Mass Rapid Transport
Bibliographic record
Abstract
This research aims to identify the factors, including service quality, corporate Image, and perceived Value, that contribute to Mass Rapid Transit Jakarta's customer satisfaction. Mass Rapid Transit is a mass transportation that has become necessary due to the prevalence of private automobiles in Jakarta. This study employs a descriptive quantitative methodology using a survey of 165 Mass Rapid Transit passenger respondents and descriptive statistical analysis and modeling with the Structural Equation Modeling-Partial Least Square. The results showed that the Quality of Service has a positive effect on passenger satisfaction, the corporate image does not affect passenger satisfaction, the perceived value has a positive contribution to passenger satisfaction, and the Quality of Service has a positive effect on the perceived value. Furthermore, the corporate image positively contributes to perceived value, service quality positively affects customer satisfaction mediated by perceived values, and the corporate image does not affect passenger satisfaction mediated by perceived value. Therefore, mass Rapid Transit Jakarta needs to make various innovations to improve service quality mediated by service quality dimensions that refer to service quality. In addition, the human capital of service officers at Mass Rapid Transit Jakarta needs to be improved in terms of Quality and competency so that passengers' opinions of the service staff are more favorable, increasing both perceived value and customer 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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".