Brand Awareness as a Moderator in Customers’ Perceived Value and Brand Loyalty
Bibliographic record
Abstract
Ms. Kai Jiang is currently a year one Ph.D student at the department of Recreation and Leisure Studies in University of Waterloo in Canada. Before joining UWaterloo, she worked as a project associate in China Business Center, the Hong Kong Polytechnic University (PolyU) for two years, responsible for various consultancy projects on SME brand development, cosmetic industry brand strategy and jewelry industry brand promotion, etc. She also worked in hotel industry for one year. She graduated from PolyU with the degree of Master of Philosophy (M.Phil) majoring in tourism and hospitality management. She has accumulated extensive empirical research experience in SEM modeling, consumer behavior & psychology, customer service, business and sports events management. Besides, she has worked in various international organizations like the Olympic Committee and the organizing committee of Universiade 2011. In 2010, she was designated as a member of Meeting Professionals International (MPI), holding the Global Certificate in Meetings and Business Events.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".