The relative impacts of experiential and transformational benefits on consumer-brand relationship
Bibliographic record
Abstract
Purpose This paper aims to explore and compare the roles of brand’s experiential and transformational benefits in formation of consumer-brand relationships. Focusing on cosmetics consumption, the study investigates how brand’s experiential benefits (brand experience) and transformational benefits (self-esteem and self-expression) could impact the strength of consumer-brand relationships. Design/methodology/approach Data analysis was performed using structural equation modeling technique. The sample consisted of 373 university students, who completed self-administered questionnaires. Findings Results show that brand experience and self-expression have significant positive impacts on consumer-brand relationships. Brand experience plays a more important role, compared with transformational benefits, in this process. Theoretical and managerial implications are discussed. Research limitations/implications Future research could study possible transformative experiences across various industries. It could also use a more divergent sample that better represents general population. Originality/value This study is among the first in the literature to investigate the impacts of emerging sources of brand value, i.e. experiential and transformational benefits, in formation of strong consumer-brand relationships. It is also among the first to compare the predictive power of those two types of benefits in shaping brand-related outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".