Factors Affecting Customer Satisfaction in Purchasing Car
Bibliographic record
Abstract
Globalization of the markets coupled with economic downturn had changes the pattern of customer behavior and consumption patterns. The customer buying behavior is a complex topic as many internal and external factors have impact on the level of satisfaction of the customer. From the past decade, previous researchers had attempted to understand how customers’ needs their responses and feedbacks. In 2017, the number of Honda Civic 2017 Model car owners has reached 109,511 units in Malaysia and the Malaysia’s southern region occupied 34% of Honda’s total sales compared to others car model (Honda, 2018; Lye, 2018). Seeing that the demand for this model is high, it is crucial to study the car owners’ satisfaction align with the automobile and organizational standards, especially through effective customer satisfaction measurement model. The objective of the research is to study the relevant factors that affecting customers’ satisfaction in purchasing Honda Civic car (model 2017). This research studied the factors (price, customers’ services, brand image and quality) in influencing the customers’ satisfaction. The results show that customers’ services and quality have significant relationship towards customers’ satisfaction. The findings would be useful for academicians to further study on factors related with this area or to find out whether similar to apply this to other industry as well.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".