A hybrid of Kano and QFD for ranking customers’ preferences: A case study of bank Melli Iran
Bibliographic record
Abstract
Nowadays, many service provider organizations compete to survive and surpass other competitors in the world. They apply new techniques and instruments to identify and to prioritize important criteria for their customers to gain customer satisfaction. Banks, as one of the service provider organizations, are no exception. Quality plays essential role in banking industry and customer' gratification is considered as one of the major and essential goals in this field. Recognition and awareness regarding the customers' needs and requirements would facilitate providing satisfactory services. It could be said that improved understanding, accurate identification and prioritization of bank customers' requirements are the keys to success for bank managers. The present study aims to integrate two approaches of Quality Function Deployment (QFD) and Kano's model through implementation of Analytical Hierarchy Process (AHP). This study proposes a novel approach to identify and to analyze the priorities of bank customers' requirements. The results indicate that the priorities of bank customers are different before and after integration of Kano's Model in the planning matrix of QFD.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".