How Attitudes Translate to Loyalty: An Integrative Model in Service Relationship Marketing
Bibliographic record
Abstract
Enhancing customer loyalty is the ultimate goal of relationship marketing. While prior studies have highlighted the significance of hedonic and utilitarian attitudes as key drivers of consumer behavioral loyalty, literature, especially in the service context has left ambiguities regarding: (1) integrative exploration of mechanisms mediating attitudes’ impact on behavioral loyalty; and (2) the specific components and connections between attitudinal and behavioral loyalty, along with their origins. This study introduces a novel integrative model that delves into the distinct effects of hedonic and utilitarian attitudes on components of attitudinal and behavioral loyalty, shedding more light on understanding the interplay between these components, their relationships, and their functional connections with trust and attitudes. Structural Equation Modeling was applied to the survey results from 1,028 participants, with the results demonstrating how hedonic and utilitarian attitudes differently impact various components of behavioral loyalty through mediation of trust and attitudinal loyalty components. Furthermore, results show that relationships varied across different services. The findings will help service firms to identify important predicting factors and channels of service behavioral loyalty, thus enabling them to optimize the costs of customer-service relationship management.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".