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Record W2111900000 · doi:10.1177/1938965512443347

Scripting Employees

2012· article· en· W2111900000 on OpenAlexaff
Liana Victorino, Alexander R. Bolinger

Bibliographic record

VenueCornell Hospitality Quarterly · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsScripting languageService (business)Task (project management)HospitalityHospitality industryNoticeBusinessMarketingCustomer relationship managementService recoveryOrder (exchange)Computer scienceTourismService qualityManagement

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.004

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.

Opus teacher head0.032
GPT teacher head0.240
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2012
Admission routes1
Has abstractyes

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