Can Customers Detect Script Usage in Service Encounters?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Service scripts are predetermined guides for employees to follow when delivering service to customers. Some services require employees to strictly follow a script, whereas others use scripts more flexibly, if at all. Extant research regarding service scripts in the domain of service operations has mainly addressed the topic from more of a process view as a control mechanism for the encounter but minimal research has examined customer perceptions of scripted service. The authors examine a pivotal first question, which is if customers can detect different approaches to script use. To answer the question, the authors conducted a video experiment of face-to-face service encounters in the hospitality industry. The results indicate that customers can detect degrees of script use across both standardized and customized encounter types. This work serves as initial empirical evidence that customers are indeed capable of detecting subtleties in scripting approaches in different service situations and supports that script level is an important service design construct for research. Furthermore, the authors highlight the use of a video experiment as an innovative methodology for assessing customer perceptions of intangible aspects to services in a realistic setting. One implication of this study is that managers need to assess the impact that different script levels have on customer perceptions of various service performance measures. Managers should also consider the effect script detection has on customer perceptions of the service experience and service brand to assure their script approach aligns with the organization’s service strategy.
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.
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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it