This study is to examine the intention of the elderly who live alone in the customized AI speaker for the elderly living alone to improve the quality of life service for the elderly living alone in the smart city environment. Based on the quality of life model of the elderly, this study is applied to the technology acceptance model to investigate the relationship between perceived usefulness and ease of use on the sustained use intention. Research design, data and methodology: Residents in Suwon, Gyeonggi-do, selected as candidate local governments for the Smart City Challenge Project of the Ministry of Land, Infrastructure and Transport in June 2019 to measure the perceived technology acceptance of potential users for the AI technology for the elderly living alone as part of the smart city technology. In order to evaluate the intention of using AI speaker, which is the target system of this study, a video of a chatbot using experience of elderly people living alone was produced. Results: First of all, in order for the elderly living alone to have an attitude to use AI-based speakers, there should be a perceived usefulness of the quality of life of the elderly. However, ease of use did not show any significant causal relationship to attitude toward use. In addition, the attitude toward use weakly influenced the intention to use. In other words, elderly people living alone were not likely to have a significant effect on their attitude toward use. However, feeling that AI speakers are easy to use will help to improve the quality of life, which in turn led to the attitude toward using AI speakers, which could lead to indirect effects. Finally, the perceived usefulness of quality of life was found to have a weak effect on direct use intentions. Conclusions: This study conducted a study on the technology acceptance of service environment to improve the quality of life for the specific user group who live alone in the smart seat environment. In this study, we examined the effects of AI speaker on the elderly living alone to improve the quality of life for the elderly living alone.

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Purpose -To meet the needs of various customers in an uncertain market environment, many companies use product modularization strategies. Modularization of a product means that one product consists of several components and that the type of product can be changed according to the combination of components. The greatest feature of modularity is that changes in one component do not significantly affect the physical changes in the other component to which they are connected. Modularization of products is recognized as a very important strategy to reflect increasingly complicated customer requirements to products and respond to the needs of various markets. Many studies have been made in connection with the concept of mass customer satisfaction. There are many prior studies that modularization of such products positively affects the operational performance (manufacturing cost, fast delivery, etc.) and innovation of the product. However, excessive modularization has been found to have a negative effect on this performance. However, there are very few studies on the nonlinear relationship between product modularization and customer satisfaction. Supplementing these academically insufficient parts is very necessary when considering the current market environment. Research design, data, and methodology -In order to make up for the shortcomings of academic research in Korea, this study collects data through questionnaires in electronic, auto, and defense industry. This is because these industries are using modularity of products. based on lots of previous studies and information overload theory, we made two hypothesis and verify with empirical analysis. All 108 data were used. We used the R program and SPSS program for statistical verification.Results -As a result of the study, modularization of products showed positive relationship with customer satisfaction to a certain level. However, it has been found that when the modularization is over and beyond a certain level, there is a negative relationship with customer satisfaction. Conclusions -Excessive modularization of products can have a negative impact on customer satisfaction. This result can be understood as a result of human limited rationality due to information overload. Therefore, it is important for companies to apply appropriate modularity to product design.

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Purpose: In modern society, many urban problems are occurring, such as aging, hollowing out old city centers and polarization within cities. In this study, we intend to apply big data and machine learning methodologies to predict depression symptoms in the elderly population early on, thus contributing to solving the problem of elderly depression. Research design, data and methodology: Machine learning techniques used random forest and analyzed the correlation between CES-D10 and other variables, which are widely used worldwide, to estimate important variables. Dependent variables were set up as two variables that distinguish normal/depression from moderate/severe depression, and a total of 106 independent variables were included, including subjective health conditions, cognitive abilities, and daily life quality surveys, as well as the objective characteristics of the elderly as well as the subjective health, health, employment, household background, income, consumption, assets, subjective expectations, and quality of life surveys. Results: Studies have shown that satisfaction with residential areas and quality of life and cognitive ability scores have important effects in classifying elderly depression, satisfaction with living quality and economic conditions, and number of outpatient care in living areas and clinics have been important variables. In addition, the results of a random forest performance evaluation, the accuracy of classification model that classify whether elderly depression or not was 86.3%, the sensitivity 79.5%, and the specificity 93.3%. And the accuracy of classification model the degree of elderly depression was 86.1%, sensitivity 93.9% and specificity 74.7%. Conclusions: In this study, the important variables of the estimated predictive model were identified using the random forest technique and the study was conducted with a focus on the predictive performance itself. Although there are limitations in research, such as the lack of clear criteria for the classification of depression levels and the failure to reflect variables other than KLoSA data, it is expected that if additional variables are secured in the future and high-performance predictive models are estimated and utilized through various machine learning techniques, it will be able to consider ways to improve the quality of life of senior citizens through early detection of depression and thus help them make public policy decisions.