Effect of Customer Satisfaction on Brand Image & Loyalty Intention : A Study of Cosmetic Product
Bibliographic record
Abstract
The Purpose of the study was to investigate the effect of customer satisfaction on brand image and Loyalty intention directly and indirectly based upon hypothetical model in the current study for a cosmetic brand (Fair lovely) at Gwalior (M.P) in India. The measures were restandardized to make it suitable for the purpose of the study. Number of factors were identified through Exploratory factor analysis for all the variables. Structual Equation Modeling was used in the current study through AMOS 16. The results of SEM indicate that there is strong relationship between customer satisfaction and brand image. The result of SEM also indicates that there is strong relationship between Brand Image and Loyalty intention and the relationship between Customer satisfaction and Loyalty intention was found little weak. While the indirect relationship between customer satisfaction and loyalty intention via brand image was found to be very strong. The measure of benefit of brand image was constituted of Functional, Social, Symbolic, experiential and appearance enhance. A survey was carried out using 250 respondents. The results also indicated that overall satisfaction does influence customers' loyalty which imply that marketers should focus on brand image benefits to achieve customer loyalty.
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 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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".