Peak Event Self-Scheduling: Implications for Service Demand Management
Why this work is in the frame
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Bibliographic record
Abstract
Services are often segmented into discrete events, allowing customers to self-schedule their own itinerary. We term this behavior as self-scheduling . We theorize that customers self-schedule peak events—those they predict will be their most salient—at predictable points, primarily for the bookends (i.e., at the beginning or the end). This behavior can create demand fluctuations and pose challenges for demand management. To examine this phenomenon, we conducted two exploratory studies. First, a survey using a tour context revealed a preference for self-scheduling the peak event at the beginning. A more balanced distribution between bookends emerged when information promoting the peak event was provided. Second, wait-time data from three major theme parks in the United States was collected during the summer of 2024 and validated that customers predominantly self-schedule peak events for the beginning. Next, we hypothesized how information provision may influence customers’ self-scheduling behavior of a peak event. A scenario-based experiment and a conjoint study in a theme park context found that practices enhancing perceived control (i.e., wayfinding and wait line management information) effectively shifted some of the demand from the beginning to later in a visit. We discuss insights to support demand management in self-scheduling service contexts.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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