Investigating preferences and influencing factors of age-friendly housing environments across different age groups using the Delphi-Kano model and entropy weight TOPSIS method
Erxin Tu , Ying Zhou
Front. Archit. Res. ›› 2025, Vol. 14 ›› Issue (4) : 899 -913.
Investigating preferences and influencing factors of age-friendly housing environments across different age groups using the Delphi-Kano model and entropy weight TOPSIS method
China's escalating aging population has made home-based care the primary means of elderly support. However, current residential environments frequently fail to meet seniors' needs. Research on elderly housing lacks comparative studies across different demographic groups. This study employs the Delphi method to identify improvements for Age-Friendly Housing (AFH) and develops a Kano model questionnaire to analyze preferences among various age groups of elderly individuals. Additionally, it evaluates how living arrangements and health conditions impact these preferences. Results show that individuals aged 60-74 prioritize visual connections with the external environment and monitoring the surroundings outside their homes, while those aged 75 and above focus more on bathroom safety and indoor mobility. Seniors aged 60-74 living with children, and those aged 75 and above living with caregivers or in nursing homes, generally exhibit less concern for most design strategies. Conversely, seniors with arthritis, visual impairments, or hearing disabilities tend to favor design improvements more. Policymaking and implementation related to AFH should account for elderly individuals' diverse ages and living arrangements to set appropriate priorities, thereby aiding in optimizing resources and enhancing residents' satisfaction and quality of life.
Age-friendly housing design strategies / Chinese old adults / Delphi-Kano model / Entropy weight TOPSIS analysis / Living environment for old adults / Prioritization
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The Author(s). Publishing services by Elsevier B.V. on behalf of Higher Education Press and KeAi.
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