Using neighborhood rough set theory to address the smart elderly care in multi-level attributes

Zhou, J., Zhang, B., Tan, R., Tseng, M.-L., Lin, R.C.-W. and Lim, M.K. (2020) Using neighborhood rough set theory to address the smart elderly care in multi-level attributes. Symmetry, 12(2), 297. (doi: 10.3390/sym12020297)

Abstract

The neighborhood rough set theory was adopted for attributes reduction and the weight distribution of condition attributes based on the concept of importance level. Smart elderly care coverage rate is low in China. A decisive role in the adoption of smart elderly care is still a problem that needs to be addressed. This study contributes to the adoption of smart elderly care was selected as the decision attribute. The remaining attributes are used as conditional attributes and the multi-level symmetric attribute set for assessing acceptance of smart elderly care. Prior studies are not included smart elderly care adoption attributes in multi-levels; hence, this problem needs to be addressed. The results of this study indicate that the condition attribute of gender has the greatest influence on the decision attribute. The condition attribute of living expenses for smart elderly care has the second largest impact on decision attribute. Children’s support for the elderly decency of the novel elderly care system and the acceptance of non-traditional elderly care methods belong to the primary condition attribute of traditional concept. The result indicates traditional concepts have a certain impact on the adoption of smart elderly care and a condition attribute of residence also has a slight influence on the symmetric decision attribute. The sensitivity analysis shows the insights for uncertainties and provides as a basis for the analysis of the attributes in the smart elderly care service adoption.

• Item Type: Articles
• Additional Information: This study is funded by [National Natural Science Foundation of China] grant number [51675159]. This study is partially funded by 109-2918-I-468-001-Taiwan.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Lim, Professor Ming
• Authors: Zhou, J., Zhang, B., Tan, R., Tseng, M.-L., Lin, R.C.-W., and Lim, M.K.
• College/School: College of Social Sciences > Adam Smith Business School > Management
• Journal Name: Symmetry
• Publisher: MDPI
• ISSN: 2073-8994
• ISSN (Online): 2073-8994
• Copyright Holders: Copyright © 2020 by the authors
• First Published: First published in Symmetry 12(2):297
• Publisher Policy: Reproduced under a Creative Commons licence