The Influence of Online Reviews and Brand Trust and Customer Equity
Bibliographic record
Abstract
Using a restricted probability sample of 269 participants, the key findings were: (a) that negative online reviews have a higher negative impact on customer equity than positive online reviews; this is a significant finding because previous findings were mainly short-term focus (on willingness to purchase) and long-term measurement such as ‘customer equity' could provide management with new knowledge on negative online reviews; (b) ‘brand equity' driver has the greatest impact on customer equity as compared to the other two drivers (‘value' and ‘relationship'). This is a significant new finding which could assist management in redirecting its resources; (c) brand trust was unexpectedly found to have a negative relationship with the drivers of customer equity. This could be that long-term outcome as brand trust may prove to be difficult to measure in a cross-sectional study. Furthermore, online reviews have no significant relationship with ‘brand trust.' This may be that brand trust may be more important in short-term outcomes with online reviews.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".