• Science of Emotion and Sensibility
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• v.25 no.1
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• pp.29-40
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• 2022

This study uses the bidet 4D double diamond design process model to propose an improvement for "senior-oriented bidet product service design" that reflects the characteristics and needs of seniors. This study was based on the product service system concept. To this end, qualitative research on seniors was conducted to derive user value factors, and, based on this, product service ideas were discovered, and a prototype reflecting the usefulness review of a working-level expert group was proposed. First, a "smart application service for user-customized function setting guide" was proposed. A bidet incorporating Internet of Things technology and a smart phone are linked to provide an app service that automatically interprets user characteristic information and information on bidet products to guide customized functions. Second, a control panel and remote control user interface to "user-oriented product service interface" was proposed. In consideration of the usability and cognitive ability of seniors, a simple and intuitive physical user interface such as a configuration centered on main functions, button arrangement according to task sequence, and a touch screen remote control was presented. Third, we proposed a "bidet care service linked with products and health/hygiene care" that provides a wide range of services such as user health and hygiene, cleanliness, entertainment, etc., in addition to regular bidet product service. This study proposed a product-based service design methodology that can improve user experience and relationship quality by discovering and improving the pain points and needs of users (seniors) in the process of using bidet products (before, during, and after use).

• 한국노년학
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• v.31 no.1
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• pp.79-96
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• 2011

The purpose of this study was to search the clustering and variables in discriminating the groups by the level of the loss experience and ego-integrity among the elderly. In addition, the study aimed to confirm the moderating effects of variables related to resilience in the relationship between a loss experience and ego-integrity. The subjects were 245 elderly aged 60 or above, most of whom were socially active with comparatively high education levels. Six individual variables(physical self-efficacy, general self-efficacy, social self-efficacy, existential spirituality, religious spirituality, optimism) and five environmental variables(emotional support, instrumental support, informational support, appraisal support, social activity) were used in the study. The results of the study are as follows. To data analysis, ANOVA, Stepwise Discriminant analysis, Cluster analysis, Regression analysis and Two-way analysis of variance was employed. First, the subjects were 4 cluster into overcome group, crisis group, stable group and vulnerable group according to the level of loss experience and ego-integrity. Second, optimism, physical self-efficacy, existential spirituality and emotional support were seen to be significant variables in discriminating the groups. Third, the ego-integrity became higher as the variables related to resilience were increased.

• Journal of Venture Innovation
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• v.6 no.4
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• pp.83-97
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• 2023

The growth of the live commerce market allows you to conveniently and simply start live commerce anytime, anywhere with a smartphone. The use of smartphone services provides continuous communication and is used while feeling psychological attachment, and it leads to psychological attachment, self-consistency with consumers themselves, and self-identity. This study focuses on the motives and perceptions of consumers using live commerce. In other words, we will examine the relationship with service attachment through the moderating effect of self-efficacy and control focus tendency as consumers' personal and psychological characteristics. In other words, the tendency of regulatory focus, which determines the direction of behavior of consumers according to their motives and goals, affects the service attachment of live commerce. We believe that self-efficacy, which is personal confidence and belief that you can plan and execute on your own for the desired outcome in a given situation or task, will control this relationship. As a result of this research, consumers who highly perceive prevention focus were more likely to avoid negative consequences and pursue safety and obligations. Their attachment to live commerce services was stronger, offsetting their confidence and self-efficacy. When using live commerce services, the more they perceive that information acquisition is beneficial, the higher their belief, and self-efficacy, so service attachment, which is an emotional experience as well as a cognitive experience, is strongly formed for consumers with a preventive focus to avoid safety-seeking and negative consequences. Through the present research results, we believe that it will be helpful in operating strategies and management for companies and small business owners who want to understand the psychological behavior of consumers in using live commerce services.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.