The psychology of dynamic pricing: how personality traits shape fairness perceptions and purchase intentions in tourism
Bibliographic record
Abstract
The growing adoption of dynamic pricing in tourism and beyond contrasts with our limited understanding of how individual differences affect customer responses. This study examines how personality traits influence price unfairness perceptions of revenue management and purchase intentions in the alpine skiing industry. We conduct two studies: a large-scale correlational survey of 1,627 skiers from Austria, Canada, Norway, and the United States, and a short, quasi-experimental follow-up study with 206 participants from Italy and the United States. Across the two studies we find that agreeableness and openness reduce, whereas neuroticism increases, the perceived unfairness of revenue management. This perceived unfairness of revenue management has important consequences: reduced purchase intent and an increased desire for revenge. This study shows that personality traits, although not directly observable, strongly predict behavioral intentions. They can be inferred from the ways customers react to managerial actions within a firm’s control. The study contributes to research on the antecedents of price fairness and to broader debates on personality traits and the Five-Factor Model. Practical implications include opportunities for more effective personalized pricing strategies. Implementation must be guided by transparency and robust ethical safeguards.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".