Locational Choices: Modeling Consumer Preferences for Proximity to Others in Reserved Seating Venues
Bibliographic record
Abstract
This article proposes a measurement approach to determine how consumers prefer to locate themselves in proximity to others during consumption experiences, such as when they purchase reserved seating tickets to a performance. Applied to data from locational choice experiments that simulate reserved seating assortments, administered to more than 2,000 participants, this approach reveals the importance of modeling proximity to others when studying locational choices. It also emphasizes the degree to which consumers are heterogeneous in their preferences for proximity to both focal elements (e.g., stage, screen, aisles) and other consumers. Therefore, event operators should collect data beyond purchase ticket logs and also include consumers who did not purchase. Furthermore, this study illustrates how managers can use fitted, individual-level parameters and an optimization model to make more effective seat-level availability decisions. In addition to these recommendations for managers of reserved seating venues, this article offers novel contributions to research related to advance selling, spatial models, and personal space.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".