A hybrid model of QFD, SERVQUAL and KANO to increase bank's capabilities
Bibliographic record
Abstract
In global market, factors such as precedence of competitors extending shave on market, promoting quality of services and identifying customers' needs are important.This paper attempts to identify strategic services in one of the biggest governmental banks in Iran called Melli bank for getting competition merit using Kano and SERVQUAL compound models and to extend operation quality and to provide suitable strategies.The primary question of this paper is on how to introduce high quality services in this bank.The proposed model of this paper uses a hybrid of three quality-based methods including SERVQUAL, QFD and Kano models.Statistical society in this article is all clients and customers of Melli bank who use this banks' services and based on random sampling method, 170 customers were selected.The study was held in one of provinces located in west part of Iran called Semnan.Research findings show that Melli banks' customers are dissatisfied from the quality of services and to solve this problem the bank should do some restructuring to place some special characteristics to reach better operation at the heed of its affairs.The characteristics include, in terms of their priorities, possibility of transferring money by sale terminal, possibility of creating wireless pos, accelerating in doing bank works, getting special merits to customers who use electronic services, eliminating such bank commission, solving problems in least time as disconnecting system, possibility of receiving foreign exchange by ATM and suitable parking in city.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".