Brand Equity and Its Elements: Case of the Lake Balaton (Hungary)
Bibliographic record
Abstract
The article provides a valuable insight into understanding brand equity and its elements that endows the destination with a unique character. Using empirical data, the study seeks to identify potential brand equity items of a destination type, namely waterside area (Lake Balaton, Hungary). The innovative approach of the research is its holistic view, because it integrates the customer-based brand equity with stakeholders’ (including media and tourism professionals) perceptions. This enables a complex understanding of the researched topic, the conclusions are summarized in the Five-Stage Brand Pyramid model. The results highlight four main dimension of the brand equity: (1) the fundamental role of destination specific (waterside in this case) attributes, (2) the country of origin elements (emotional and rational benefits of domestic travel in this case), (3) the tourism products/activities, and (4) the emotional dimension. Furthermore, the research also identifies some important gap between demand and supply side. The research resulted important theoretical (structure of brand equity, need for a complex methodology) and practical (strong emotional perceptions of consumers, comprehensive analysis of tourism products/activities) implications that can be scope of further researches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".