The mediating effect of organizational reputation on customer loyalty and service recommendation in the banking industry
Bibliographic record
Abstract
Purpose The overall purpose of this study was to develop an understanding of the mediating effect of organizational reputation on service recommendation and customer loyalty. Design/methodology/approach Four models were developed that were variations of the American Customer Satisfaction Model (ACSM). These models were then tested by using the Partial Least Squares (PLS) procedure on a data collected from a survey that yielded 8,098 respondents. Findings It was found that customer satisfaction enhances reputation in the service environment. It was also discovered that reputation partially mediates the relationship between satisfaction and loyalty, and that reputation partially mediates the relationship between satisfaction and recommendation. Research limitations/implications More research needs to be undertaken to explore the role of reputation within the ACSM. It is necessary to conduct research employing experimental design with longitudinal data captured from across industries using robust measures. Originality/value The findings suggest that the relationship between corporate reputation and profitability may reside in reputation's influence on customer loyalty, and that reputation plays an important role within the ACSM. This study is one of the first documented attempts to use PLS to test a mediation effect.
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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 | 0.003 | 0.017 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".