Modeling the effects of a Service Guarantee on Perceived Service Quality Using Alternating Conditional expectations (Ace),” Decision Sciences
Bibliographic record
Abstract
This paper addresses the dearth of empirical research on the relationship between service guarantee and perceived service quality (PSQ). In particular, we examine the moderating effects of a service guarantee on PSQ. While a recent study provided empirical evidence that service quality is affected by service guarantee and employee variables such as employee motivation/vision and learning through service failure, the nature and form of the relationships between these variables remain unclear. Knowledge of these relationships can assist service managers to allocate resources more judiciously, avoid pitfalls, and establish more realistic expectations. Data was obtained from employees and customers of a multinational hotel chain that has implemented a service guarantee program in 89 of its hotels in America and Canada. As the employee variables could affect performance in a non-linear fashion, we relaxed the assumption of model linearity by using the Alternating Conditional Expectations (ACE) algorithm to arrive at a better-fitting, non-linear regression model for PSQ. Our findings indicate the existence of significant non-linear relationships between PSQ and its determinant variables. The ACE model also revealed that service guarantee interacts with the employee variables to affect PSQ in a non-linear fashion. The non-
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".