Waiting in a queue with strangers and acquaintances
Bibliographic record
Abstract
Purpose The purpose of this paper is to discuss the effect of social exchanges between customers that may occur in a queue, on the waiting experience's evaluation and its implication for the customer service management. Design/methodology/approach Extant literature on social exchanges between customers within consumption environment is reviewed pertaining to the interrelationships between customer‐to‐customer interactions, atmospherics' perception and waiting time evaluation. A conceptual model is built upon the reviewed literature illustrating the relationships between main concepts of the study. Findings The insights from this work suggest that making interactions between customers more enjoyable may reduce waiting time perception. In contrast, if the customer‐to‐customer interaction is perceived as negative, this may increase the waiting time evaluation. Research limitations/implications Albeit conceptual and exploratory in nature, this paper is intended as a beginning for further empirical validation of the effect of customer‐to‐customer interaction on the waiting experience. Originality/value Few studies have investigated explicitly the impact of customer‐to‐customer interactions on waiting time evaluation. This paper suggests that social exchanges that may occur in the queue may affect the customer's waiting experience.
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 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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".