Performance measurement of the after-sales service network: Evidence from the automotive industry
Bibliographic record
Abstract
This paper presents an empirical investigation to determine important factors influencing on customer satisfaction in after-sales service network of automotive industry. The study designs two questionnaires, one for measuring the quality of after-sales services and the other for measuring customers' satisfaction. The study selects a sample of 265 randomly selected customers out of 850 people who received the services from an automotive firm in Iran. Cronbach alpha has calculated as 0.82, which is well above the minimum desirable level. Using Spearman correlation the study has detected a positive and meaningful relationship between services and customer satisfaction (r=0.48, Sig. =0.01), a positive relationship between being responsiveness and customer satisfaction (r=0.51, Sig. =0.01) and finally a positive relationship between speed of operation customer satisfaction (r=0.45, Sig. = 0.01). Moreover, there was a positive and meaningful relationship between cost of services and customer satisfaction (r=0.68, Sig. = 0.01) and a positive relationship between quality of services of after-sales services and customer satisfaction (r = 0.61, Sig. =0.01).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".