Bibliographic record
Abstract
Because customers can recognize when hospitality firms are employing service scripts, an examination is in order to determine how customers perceive scripted service. This article assesses customer perceptions of scripted service encounters with both content analysis and quantitative analysis of more than 2,000 open-ended customer responses to a survey addressing service scripts. The study found both task- and treatment-related implications for customer reactions to scripted service. Customers’ overall sentiment toward scripting is more positive when they are focusing on task-related outcomes, but their comments turned more negative when they considered treatment-related implications, particularly when they detected employee insincerity in regard to the script. Management should consider these findings carefully because customers in this survey gave more notice to treatment-related issues than they did to task-related outcomes for the scripted service. A key implication is that managers may wish to reserve tight scripting for simple services for which efficiency is valued, such as hotel check-in or seating guests in a restaurant. For more complex services, scripts should be flexible, and managers should seek employee buy-in so that they are internalizing the script elements, rather than merely “surface acting.”
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".