Service quality and supply chain value on customer loyalty: The role of customer relationship management
Bibliographic record
Abstract
Intense business competition urges companies to continually enhance their marketing strategies to retain and attract customers. Therefore, a profound understanding of factors influencing customer loyalty becomes crucial. Service quality, customer satisfaction, and supply chain value are considered key factors affecting customer loyalty. However, the relationships between these variables and the role of Customer Relationship Management (CRM) as a mediator need further investigation, especially in the context of Indonesian companies. Hence, this research aims to contribute a deeper understanding of the interconnection between service quality, customer satisfaction, supply chain value, and customer loyalty, as well as to explore the role of CRM as an essential link in this dynamic. The research methodology employed is quantitative, utilizing a Likert scale questionnaire distributed online to managers and employees in the automotive sector listed on the Indonesia Stock Exchange (IDX). Out of 400 distributed questionnaires, 261 were successfully collected, with 14 incomplete responses, resulting in a final sample size of 247. Data collection took place from June to August 2023. In data analysis, the study applied the Structural Equation Modeling (SEM) approach using the SmartPLS analysis tool. The research findings indicate that service quality significantly influences CRM, while it does not have a direct significant impact on customer loyalty. Customer satisfaction significantly affects both CRM and customer loyalty. Supply chain value significantly influences CRM but does not have a direct impact on customer loyalty. Customer Relationship Management proves to mediate the relationships between service quality and customer loyalty, customer satisfaction and customer loyalty, as well as supply chain value and customer loyalty.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".