Improving service quality of hospital front office using an integrated Kano model and quality function deployment
Abstract
Purpose: The aims of this study are twofold: first, it attempts to investigate service attributes in a hospital front office; and second, to identify strategies to improve those service attributes.
Design/methodology/approach: This study used integration of Quality Function Deployment and Kano Model. The research instrument, which takes the SERVQUAL model as its starting point, was developed using a comprehensive set of techniques, including a literature review of relevant topics, interviews and focus group discussions. Using a sample of 140 customers of an international hospital situated in Yogyakarta, Indonesia, 14 service attributes required by customers were identified. The attributes, which were further categorised into 5 attractive, 4 one-dimensional and 5 ‘must-be’ attributes, were analysed using the Kano Model.
Findings: Using the integrated QFD and Kano Model, the service attributes needed for improvement were identified. The results are different from those when the company used either SERVQUAL or QFD alone. This study also reveals that benchmarking with competitor might produce misleading results. The results are different when the analysis combined a comprehensive method of QFD and Kano Model.
Practical implications: Service providers will benefit from the findings of this study, as both the service attributes and technical requirements that require improvement as a priority are identified.
Originality/value: It is the first time that front office quality of hospital is examined using integrated method of SERVQUAL, QFD and Kano Model. The recommendations proposed from this comprehensive method offer novel solution that has never been found in existing study.
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DOI: https://doi.org/10.3926/ic.1001
This work is licensed under a Creative Commons Attribution 4.0 International License
Intangible Capital, 2004-2024
Online ISSN: 1697-9818; Print ISSN: 2014-3214; DL: B-33375-2004
Publisher: OmniaScience