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Evaluation of service quality using SERVQUAL scale and machine learning algorithms: a case study in health care
Kybernetes ( IF 2.5 ) Pub Date : 2021-07-14 , DOI: 10.1108/k-10-2020-0649
Serkan Altuntas 1 , Türkay Dereli 2 , Zülfiye Erdoğan 3
Affiliation  

Purpose

This study aims to propose a service quality evaluation model for health care services.

Design/methodology/approach

In this study, a service quality evaluation model is proposed based on the service quality measurement (SERVQUAL) scale and machine learning algorithm. Primarily, items that affect the quality of service are determined based on the SERVQUAL scale. Subsequently, a service quality assessment model is generated to manage the resources that are allocated to improve the activities efficiently. Following this phase, a sample of classification model is conducted. Machine learning algorithms are used to establish the classification model.

Findings

The proposed evaluation model addresses the following questions: What are the potential impact levels of service quality dimensions on the quality of service practically? What should be prioritization among the service quality dimensions and Which dimensions of service quality should be improved primarily? A real-life case study in a public hospital is carried out to reveal how the proposed model works. The results that have been obtained from the case study show that the proposed model can be conducted easily in practice. It is also found that there is a remarkably high-service gap in the public hospital, in which the case study has been conducted, regarding the general physical conditions and food services.

Originality/value

The primary contribution of this study is threefold. The proposed evaluation model determines the impact levels of service quality dimensions on the service quality in practice. The proposed evaluation model prioritizes service quality dimensions in terms of their significance. The proposed evaluation model finds out the answer to the question of which service quality dimensions should be improved primarily?



中文翻译:

使用 SERVQUAL 量表和机器学习算法评估服务质量:医疗保健案例研究

目的

本研究旨在提出一种医疗保健服务质量评价模型。

设计/方法/方法

在本研究中,提出了一种基于服务质量度量(SERVQUAL)量表和机器学习算法的服务质量评价模型。首先,影响服务质量的项目是根据 SERVQUAL 量表确定的。随后,生成服务质量评估模型以管理分配以有效改进活动的资源。在此阶段之后,进行分类模型的样本。机器学习算法用于建立分类模型。

发现

建议的评估模型解决了以下问题:服务质量维度对服务质量的潜在影响水平是什么?哪些服务质量维度应该优先考虑哪些服务质量维度应该主要改进?我们在一家公立医院进行了真实案例研究,以揭示所提出的模型是如何工作的。从案例研究中获得的结果表明,所提出的模型可以很容易地在实践中进行。还发现在进行案例研究的公立医院中,在一般身体条件和餐饮服务方面存在显着的服务差距。

原创性/价值

这项研究的主要贡献有三方面。所提出的评价模型确定了服务质量维度在实践中对服务质量的影响程度。建议的评估模型根据重要性优先考虑服务质量维度。所提出的评估模型找出了应主要改进哪些服务质量维度的问题的答案。

更新日期:2021-07-12
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