Locational Choices: Modeling Consumer Preferences for Proximity to Others in Reserved Seating Venues
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it