A strategic service quality approach using analytic hierarchy process
Bibliographic record
Abstract
Purpose The paper aims to develop a technique that considers competition using the analytic hierarchy process (AHP) framework to measure service quality. Design/methodology/approach The present study adapted the AHP methodology to the measurement of service quality, involving five steps – referred to as “analytical hierarchy process for service quality” (“AHP‐SQ”). Subsequently, the authors demonstrate how the technique can be applied to the fast‐food restaurants. Findings The AHP‐SQ approach described in this study thus assists management to devise and maintain a relevant, competitive plan for ongoing improvements in service quality. Specifically, such analysis enables the following questions to be addressed: “How does the firm perform in terms of service quality in relation to its competitors?”; “Given the firm's resources, which service initiatives will enhance its service competitiveness?”; “Which service areas require immediate improvement?”; “How should the firm's service improvement be prioritized?”, and “What opportunities exist for service improvement in relation to the competition?” Research limitations/implications It would be important to consider the “right” dimensions of service quality that are relevant to the respective industry. It would also be essential to collect responses from customers who have utilized the services of the focal firm as well as its competitors in order to have an accurate opinion. Practical implications The framework proposed here allows management to address two main issues pertaining to its competitive advantage: establishing its performance ranking in the marketplace; and identifying the service elements that most require improvement. Originality/value The paper develops a cohesive approach to help managers identify which reliability, assurance, tangibles, empathy, responsiveness (RATER) service dimensions require attention to create a sustainable competitive advantage. It offers a “bigger picture” in service‐quality management.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".