Brand Attachment on Service Loyalty in Banking Sector
Bibliographic record
Abstract
The purpose of this study is to examine the effects of brand attachment on service loyalty to the services provided by financial sector. As one of the extremely valuable assets of every firm is its brand, attachment creates a deep emotional link between the consumers and the brand such that it contributes to the success of brand management process. To this end, the effects of two dimensions of the construct (brand-self connection and brand prominence) on each of the dimensions of service loyalty would be explored. The questionnaire is based on Park et al. (2010) and Sudhahar et al. (2006). The results of structural equations modeling indicated that brand attachment had a significant positive effect on service loyalty. Furthermore, the existed a positive effect on the dimensions of brand attachment—i.e., brand-self connection and brand prominence—and all dimensions of loyalty—i.e., behavioral, attitudinal, cognitive, conative, affective, commitment, and trust). Among them, brand-self connection had the highest effect on cognitive loyalty, trust-based loyalty, and commitment-based loyalty while brand prominence was most effective on affective loyalty, cognitive loyalty, and trust-based loyalty.Because of the increase in the number of institutions in banking sector and the diversity of services they offer, banking managers can take the advantage of using the results of brand attachment's effect on the study variables and enhance the loyalty to their 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 | 0.001 | 0.007 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".