Issue |
RAIRO-Oper. Res.
Volume 58, Number 5, September-October 2024
|
|
---|---|---|
Page(s) | 4531 - 4551 | |
DOI | https://doi.org/10.1051/ro/2024156 | |
Published online | 21 October 2024 |
Using the hybrid undesirable network data envelopment analysis model to evaluate the efficiency of Taiwan’s social care system
Department of Business and Entrepreneurial Management, Kainan University
* Corresponding author: davy641126@yahoo.com.tw
Received:
21
August
2023
Accepted:
24
July
2024
In this study, a hybrid undesirable network data envelopment analysis model is developed for assessing the efficiency of social care systems. Studies investigating the performance of social care activities have mostly focused on a single social care type. To date, no study has proposed an integrated framework for assessing social care performance. To address this gap in the literature, the developed model incorporates four divisions for a comprehensive performance measurement: disability care, child and youth care, women care, and elderly care. In the model, the government’s expenditure on social care is the initial input, and the efficiency of social care activities in the aforementioned four divisions is evaluated in the second stage. Social care facilities and social care workers serve as the nonradial input and radial input, respectively. Care recipients and those affected by violence serve as the desirable output and undesirable output, respectively, in the second stage. The empirical results have several practical implications for Taiwan’s social care system.
Mathematics Subject Classification: 90C30
Key words: Data envelopment analysis / social care system / undesirable / hybrid measure / network model
© The authors. Published by EDP Sciences, ROADEF, SMAI 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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