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Record W2073549000 · doi:10.1108/09604520510597827

A strategic service quality approach using analytic hierarchy process

2005· article· en· W2073549000 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueManaging Service Quality · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAnalytic hierarchy processCompetitor analysisService (business)Process managementService qualityRanking (information retrieval)Service designComputer scienceQuality (philosophy)Competitive advantageService guaranteeBusinessCompetition (biology)Quality function deploymentMarketingService delivery frameworkOperations researchEngineeringNew product development

Abstract

fetched live from OpenAlex

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.

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 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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.126
GPT teacher head0.347
Teacher spread0.221 · 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