Emotional Analysis in Designing Tourism Experiences Through Neuromarketing Methods: The Role of Uncontrollable Variables and Atmosphere: A Preliminarily Study
Bibliographic record
Abstract
The role of emotions in the tourist experience is becoming increasingly important in designing experiences to guarantee maximum involvement and satisfaction for tourists/customers. Previous literature has shown how atmosphere (e.g., visual, auditory, olfactory, tactile variables) may influence consumers’ satisfaction toward the proposed tourist experience. However, in some offers (e.g., theatrical performances, theme parks, outdoor experiences), such a relationship may be influenced by the role of “uncontrollable” variables, as for those variables related to the weather condition. Though an experimental research design based on a neuromarketing tool (face-coding), this paper is aimed to shed light on those variables in influencing consumers’ emotions, and thus their satisfaction regarding their experience. More specifically, the study has been conducted by testing a non-invasive emotional analysis tool able to associate in real-time the facial expressions of the participants with the emotions captured during the performance (e.g., as for disgust, fright, anger, boredom, neutral, surprise, happiness), as well as the emotional valence such as positivity or negativity of the emotion experienced. Results enlighten the role of tourism atmosphere in positively influencing consumers’ emotions, and thus their satisfaction also explaining the role of uncontrollable variable in magnifying such effect. Essential insights for marketers and managers in designing tourism experiences are discussed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".