Impact of Brand Prominence on Customer Satisfaction: The Moderating Role of Online/Offline Environment
Bibliographic record
Abstract
The customer-brand relationship is critical to a company's bottom line, particularly in the service industry that adopts online services. Virtual interfaces are becoming a major point of consumer contact for many companies. Some traditional service quality variables that impact customer satisfaction, such as the physical appearance of buildings, staff, and equipment and the responsiveness and empathy of employees, are not apparent in this setting. However, brand prominence may play a key role in improving consumer satisfaction. To satisfy customers and survive in the marketplace, service providers employ both online and physical purchasing channels wisely. Our research attempts to fill a gap in understanding what motivates customers' contentment and, hence, their expectations by comparing performance quality when consumers buy online vs. offline store. We analyze how brand prominence affects consumer satisfaction through the moderating effect of online and offline environments. The hypotheses were empirically validated using structural equation modeling after collecting 8533 valid responses. The findings show that both direct and indirect channel-induced expectations positively affect satisfaction, with the latter having a stronger influence. The results contribute to the literature by providing empirical evidence of the effect of expectations and performance quality on consumer satisfaction and extend the expectation-confirmation theory by including brand prominence as a component. This study contributes to a better understanding of how consumer satisfaction develops in an online or offline environment.
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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 | 0.010 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".