Framework on Performance Management in Automotive Industry: A Case Study
Bibliographic record
Abstract
The purpose of this research is to identify the risks and deficiencies that affect the performance of companies that provide vehicle after-sales services. Thus, this paper highlights the results of a comparative study based on a questionnaire conducted at the level of six brands in the automotive industry. A model was developed to investigate the factors that affect the global performance of the after-sales sector and the authenticity of the information related to the issue studied. Moreover, based on the collected data, this study evaluates the strategies related to performance management used by the organizations studied. In the end, even if the results showed a score of 81% on the questionnaire, we found that companies that provide vehicle after-sales services have not implemented and do not maintain totally the strategies related to performance management. Consequently, the need for change can be emphasized. Based on the analyzed data in the second part of the paper, we identified deficiencies and risks in terms of the organization, operation and management of the service units. These results confirm that the vehicle repair service has a significant influence on employee and customer satisfaction, on the quality of the vehicles repaired and the repair completion time.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".