Service excellence models: a critical discussion and comparison
Bibliographic record
Abstract
Purpose As customer expectations expand and as product offerings hardly differ from each other, service excellence has gained in importance as a means of enhancing customer loyalty. The aim of this paper is to focus on expanding and extending what companies can do to achieve service excellence by comparing and evaluating three popular approaches to excellence. Design/methodology/approach The authors compare three of the most commonly used excellence models, Johnston's conceptualisation of service excellence, the EFQM Model as a representative of national quality award models and the Kano model, and their respective applicability and specific perspective on service excellence. The evaluation is based on theoretical arguments, criteria and on a qualitative expert study. Findings Combining the selected models provides a comprehensive approach to service excellence. Since all models are compatible and complementary with each other, the analysis provides an enhanced understanding of service excellence and also explains in which context it is most feasible to apply any of the respective approaches. Furthermore, the requirement for a genuine service excellence model becomes evident. Research limitations/implications By focusing on three specific excellence models, others such as the Canadian Quality Award and the Australian Quality Award are not considered. Furthermore, a study across industries could reveal how service excellence is achieved in each industry to then transfer this knowledge into other sectors. Practical implications By comparing the selected models, benefits of merging the individual approaches are identified. The resulting combined perspectives offered by the individual models present a more detailed insight into what management can undertake to ensure service excellence. Originality/value As no prior research has examined the relationship between the selected excellence models and their implications for providing service excellence, this present research offers an innovative approach and thus yields new insights into the conceptualisation of service excellence.
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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 | 0.065 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.023 | 0.016 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".