Improving the quality of Internet banking services: An implementation of the quality function deployment (QFD) concept
Bibliographic record
Abstract
The competition in the business world, including banking, is getting tighter. For that, every bank must think of the right strategy to win the competition. One important strategy for winning competition is to prioritize customer satisfaction which is determined by the quality of banking services offered to customers. This study aims to analyze and process proposals for improvement in service quality in terms of using internet banking services based on the Quality Function Deployment (QFD) method through the preparation of the House of Quality (HoQ). The study was conducted on 120 internet banking users at the BRI Balikpapan Branch Office in East Kalimantan. From the results of the analysis, it is known that there are 11 indicators of bank internet banking service quality that must be improved as the first priority and 4 indicators as the second priority. Based on the results of data processing using QFD through the preparation of HoQ, it is known that there are 6 priority improvements that must be made by the bank. From the results of this study, it can be seen strategies for improving internet banking services to improve the quality of internet banking services at the BRI Balikpapan Branch Office, namely the added of new online chat features, perform server maintenance, carry out enrolment of new internet banking features, evaluate the process speed of each application feature, conduct regular website feature evaluations every quarter and perform network repair.
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".