The Influence of Tourist Attraction Type on Product Price Perception and Neural Mechanism in Tourism Consumption: An ERP Study
Bibliographic record
Abstract
Background: Tourism consumption is a topic with heated debates in tourism research, and pricing tourism products is a crucial task for tourism managers. Different types of tourist attractions offer different experiences to tourists, which affect their price perceptions and purchase decisions. Methods: This study combined questionnaires and event-related potentials (ERPs) measures to explore the magnitude of psychological conflict and the degree of emotional arousal that consumers experience when faced with different prices of goods in different scenic types. Results: The questionnaire results showed that attraction type influenced consumers' price perceptions and that consumers were willing to pay higher prices for products in attractions. The ERP results implied that in the early stage of cognition, attraction type did not affect consumers' perceptual processing, while price information attracted consumers' cognitive attention. In the late stage of cognition, attraction type, and price information jointly influenced consumers' decision-making, and consumers tended to accept high prices of products in entertainment attractions and cultural attractions, but consumers were more sensitive to the price of products in cultural attractions and less tolerant to price increases. Conclusion: The study elucidated how price information influenced consumers' purchase decisions of tourism products at different stages of the dual-process theory, which can assist tourism managers in devising different pricing strategies and positioning strategies based on the attributes of attractions, to enhance product sales and revenues. This would further the vision of the World Tourism Organization (UNWTO) of "tourism fostering economic development".
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".