How Well Do Consumer-Brand Relationships Drive Customer Brand Loyalty? Generalizations from a Meta-Analysis of Brand Relationship Elasticities
Bibliographic record
Abstract
Abstract To advance understanding of how well different types of brand relationships drive customer brand loyalty and to help companies improve the effectiveness of their relationship-building investments, this article conducts a meta-analysis of the link between five consumer-brand relationship constructs and customer brand loyalty. The analysis of 588 elasticities from 290 studies reported in 255 publications over 24 years (n = 348,541 across 46 countries) reveals that the aggregate brand relationship elasticity is .439. More importantly, results demonstrate under what conditions various types of brand relationships increase loyalty. For example, while elasticities are generally highest for love-based and attachment-based brand relationships, the positive influence of brand relationships on customer brand loyalty is stronger in more recent (vs. earlier) years, for nonstatus (vs. status) and publicly (vs. privately) consumed brands, and for estimates using attitudinal (vs. behavioral) customer brand loyalty. Overall, the results suggest that brand relationship elasticities vary considerably across brand, loyalty, time, and consumer characteristics. Drawing on these findings, the current research advances implications for managers and scholars and provide avenues for future research.
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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 | 0.060 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.026 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".