The effect of customer satisfaction on their loyalty by mediating the mental image of the brand and trust using structural equations in the Banking Industry (Case study: Pasargad Bank)
Bibliographic record
Abstract
By improvement of technology and more competitive market conditions in various manufacturing and service sectors, a loyal customer is the main asset of any organization. Nowadays, in the banking sectors, due to increasing competition and the emergence of various types of financial and credit institutions, the customer has gained more status and value, and being oriented in banks according to a fundamental principle. In fact, customers are the intangible assets of banks, and the durability of banks depends on customer’s satisfaction and trust. Therefore, paying attention to customer loyalty and identifying factors that can lead to improved customer loyalty has become an essential requirement for banks. The aim of this study was to investigate the effect of customer satisfaction on their loyalty by mediating the mental image of the brand and trust in the branches of Pasargad banks in Tehran. This research is applied in terms of purpose and descriptive in terms of method and is due to the study of the simultaneous effect between several correlation variables. The statistical population in this study is all customers of Pasargad Bank branches in Tehran, 384 of who were studied by simple random sampling. The required information was analyzed through a questionnaire using descriptive and consequential statistical methods using SPSS and LISREL software. The results of path analysis using structural equations show that customer satisfaction affects the mental image of the brand, customer trust and loyalty and also, the effect of the mental image of the brand on customer trust and loyalty was confirmed and finally trust on customer loyalty also has an impact.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".