Waiting for service at the checkout
Bibliographic record
Abstract
Purpose In retail settings, customer satisfaction is generally associated with a global evaluation of the store, i.e. the store image. Waiting for service is not part of the store image dimensions, but it does play an increasingly important role in the retail experience where waits are often inevitable. The present study seeeks to investigate how waiting for service at the checkout counter influences overall satisfaction, along with the store image. Design/methodology/approach The study combines services marketing and waiting literature. Data were collected in various supermarkets in The Netherlands. A partial least squares regression technique is used to analyze the data. Findings The paper demonstrates an important and complementary role of the behavioural construct “negative response to the wait” in explaining overall customer satisfaction in a retail environment. The effect of customers' negative emotional response to the wait on satisfaction is partially mediated by store image, and explained variance in the dependent variable increases by 24 percent when the effect of the wait at the checkout is included. Research limitations/implications Measuring customer satisfaction without taking into account the effects of various waits during the retail experience will produce incomplete results. Practical implications Moreover, a range of controllable factors influences the customer's wait. Intelligently managing these factors can mitigate negative effects on customer satisfaction, or even increase the overall evaluation of the service. Specific recommendations for service managers and a research agenda are provided. Originality/value The study combines service marketing and waiting literature to address the issue of waiting in line and tests the theory with real‐world data from a field study.
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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".