Bibliographic record
Abstract
In lululemon's success of customer engagement of their brand community, ambassador's trust is assumed to be a significant factor.This research aims to discuss how would brand ambassador's trust reflects on the customer engagement level of its offline brand community, taking customers who engage in lululemon's offline brand community in North America as a research subject by reviewing the customer engagement theory.This study focuses on using the concepts of trust and customer engagement to theoretically demonstrate the relationship between trust and customer engagement with the notion that trust will arise in the presence of uncertainty and risk aversion.The relationship between trust and customer engagement with the notion that trust will arise in the presence of uncertainty and risk aversion.From the perspective of customer engagement, five factors that may impact the level of customer engagement are analyzed and compared.A questionnaire was designed by asking questions about the ambassador of Lululemon.Finally, regression analysis had been used to analyze the data in excel.The main result of this paper showed that ambassadorial characteristics, trust, and emotional engagement have a significant impact on the level of engagement.In addition, trust having the most prominent impact on the level of engagement.This research contributes to compensate for the gap and concentrate on the offline brand community, and the limitation will also be discussed in the following context.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".