Image, performance, attitudes, trust, and loyalty in financial services
Bibliographic record
Abstract
Purpose The purpose of this paper is twofold: first, to determine the extent to which hedonic and utilitarian attitudes and loyalty are influenced by perceived financial performance (PFP) and executive compensation plan image (ECPI) in financial services; second, the authors evaluate relationships among hedonic and utilitarian attitudes, trust, and loyalty. Design/methodology/approach Using a quasi-experimental design in Study 1 the authors test the relationship between antecedents (PFP and ECPI) and relational elements (attitudes, trust, and loyalty) to address the first objective. To accomplish the second objective, the authors employ structural equation modeling in Study 2 to test the relationship among hedonic and utilitarian attitudes, trust, and loyalty. Findings Study 1 confirms that PFP and ECPI positively impact both hedonic and utilitarian attitudes but do not directly affect loyalty. Study 2 demonstrates a positive association between utilitarian attitudes and trust, although the hedonic attitudes-trust relationship is negative. Hedonic attitudes are also significantly related to utilitarian attitudes. Finally, trust mediates the relationship between attitudes and loyalty. Practical implications Building customer trust is an important correlate of loyalty, and emphasizing an attribute-based aspect of perceived financial service generates greater trust compared to enhancing a non-attribute aspect (i.e. minimizing negative effects on image of executive compensation plans). Originality/value The authors link attitude research to service/relationship quality research and discover that attitudes are indirectly related to loyalty through increases in trust. The findings suggest that perceived image and performance of financial services are important to relationship quality when applied to financial services.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".