Consumers' waiting in queues: The role of first‐order and second‐order justice
Bibliographic record
Abstract
Abstract Past research on queuing has identified social justice as an important determinant of consumers' waiting experiences. In queuing settings, people's perception of social justice is affected by whether the principle of first in and first out (FIFO) has been violated. However, even when service follows the FIFO principle, waiting time may still differ from one consumer to another for various reasons. For instance, a consumer who happens to arrive in the queue after a large group of people may have to wait longer than average. In this research, it is argued that, aside from and independent of the FIFO principle, consumers also care about whether everyone spends an approximately equal amount of time waiting before availing of the product or service. When consumers perceive that they have spent more time waiting than others and when they can attribute this injustice to the service provider, they will be less satisfied with the waiting experience. It is also proposed that adherence to the FIFO principle is a more salient concern to consumers (thus termed “first‐order” justice), and equal waiting time (”second‐order” justice) matters only when first‐order justice is not an issue. Three studies support the predictions. © 2008 Wiley Periodicals, Inc.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".