In recent years, with the acceleration of the aging population in China, the demand for intelligent products among the elderly has gradually become an important variable driving economic growth. This study focuses on the current usage status and needs of intelligent products among the elderly, aiming to reveal the core demands and obstacles in the use of smart products through empirical analysis. It seeks to provide data support for optimizing age-friendly product design and improving the smart elderly care service system, thereby contributing to the high-quality development of the "silver economy." First, this study utilized Python web crawling technology to conduct data mining on relevant literature and user reviews from e-commerce platforms. Simultaneously, secondary data was obtained through literature surveys to lay the foundation for questionnaire design. The questionnaire content covered basic information about the elderly, the current usage status of smart products, product evaluations, and purchasing intentions. After adjusting the questionnaire content based on a pilot survey and determining the sample size, stratified sampling and multi-stage PPS sampling methods were employed, ultimately collecting 723 valid questionnaires. In the data analysis, this study uses various methods such as descriptive statistics, disordered multi-class classification regression, factor analysis, seM, and system clustering to analyze the price sensitivity, consumer decision-making factors and market segmentation of the elderly. The study found that the elderly have a strong willingness to learn about intelligent products, but they need more training and support. Price is an important factor affecting the purchase decision of the elderly. There are significant differences in the use status and attitude of the elderly towards intelligent products. The acceptance and demand for intelligent products vary among the elderly of different age groups, income levels, and education levels. The study also used factor analysis and hierarchical clustering methods to divide the elderly population into four groups: undeveloped consumer groups, loyal consumer groups, secondary consumer groups, and potential consumer groups, and characterized each group. The results showed that the undeveloped consumer groups had limited understanding and awareness of smart products, with passive consumption behaviors and attitudes; the loyal consumer groups had higher demand for smart products, focusing on product quality and functionality; the secondary consumer groups had conservative consumption concepts and were price-sensitive; the potential consumer groups were interested in smart products but had not yet made purchases, indicating certain consumption potential. Based on the research conclusion, this study proposes specific recommendations for the government, enterprises and consumers. The government should promote the popularization and training of intelligent products and provide financial subsidies or preferential policies. Enterprises should optimize the design of product operation processes, strengthen the integration of voice interaction functions, and adjust product pricing and promotion strategies reasonably. Consumers should actively participate in the feedback and suggestions of intelligent products and avoid blindly pursuing high-end products. Through these measures, it is expected to significantly improve the elderly's experience and satisfaction with intelligent products, promote the wider application of intelligent products among the elderly population, and ultimately achieve a win-win situation between the elderly population and intelligent product-related enterprises.
Principal Component Analysis, System Clustering, Structural Equation Modeling, Logistic Regression Model, Intelligent Elderly Care Products.
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