• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.251-258
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• 2021

This study was conducted to investigate the impact of emotional intelligence according to the level of interpersonal relation competence. For the study, data were collected from 126 students in the department of nursing at a university located in B city from November 29 to December 16, 2019, and data were analyzed by Anova and Regression analysis. The interpersonal relation competence of nursing students was classified into less than 60 and 60 or more. In the group with an average of less than 60 in interpersonal relation competence. nursing students' self-emotional expression and control of others were significant positive effecting factors for interpersonal relation competence(explanatory power, 22.5%). In the group with an average of 60 or more in interpersonal relationship competence, self-emotional expression and Motivation were significant effecting factors(explanatory power 33.6%). According to the results of this study, we propose an individual approach according to their level of interpersonal relation competence for nursing education to improve the interpersonal relation competence abilities of nursing students.

• 한국사회정책
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• v.20 no.2
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• pp.9-34
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• 2013

This study explores the moderating effects of economic stress in the relationship between organizational devotion and emotional self-sufficiency of the self-sufficient program participants. 698 persons who lived in Taegu participated in this study. Multiple regression analysis was adopted. Results indicate that higher level of organizational devotion is positively related to the higher self-sufficiency. In addition, economic stress moderated the effect of organizational devotion on emotional self-sufficiency. Practical implications of this study are two folds; One is that in order to increase emotional self-sufficiency, sense of community should be put into consideration; The other is that approach for decreasing economic stress should be included in the program for self-sufficiency. Overall, this study lays a groundwork for the self-sufficiency of the participants in the self-sufficient program.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1502-1520
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• 2021

This paper attempts to investigate Korean youth Not in Education, Employment and Training (NEET) and how daily activities and community participation may influence their positive emotions and job search desire. First, we conducted a focus group interview with 16 NEETs who participated in the Kakao NEET Company project. The project allowed participants to experience employment by founding a virtual company in which each participant selected a daily activity to perform as part of the company's operations. Second, the interview responses were categorized and assigned emotional values using the card sorting technique and multi-dimensional analysis (MDS). A total of 11 emotional values were derived through this process. Finally, a social network analysis was conducted in order to measure the density of relations among the emotional values. Results suggest that immersion, confidence, belongingness were the three highest values evaluated by participants. Furthermore, network diagrams imply that the stronger participants perceived social support and belongingness with others, the stronger their responsibility grew, further leading them to establish steady goals. In particular, the high eigenvector score for "desire for job" suggests that emotional values are sequentially connected to the immersion-social support-responsibility-goal-job desire. This sequence suggests that digital services that are developed with the aim to enhance social values such as the Kakao NEET Project may engender motivation and confidence in youth NEETs. The overall results suggest that a systematic approach to policymaking should be considered in order to provide fundamental solutions and expand opportunities for social participation and emotional comfort, as social isolation due to low self-esteem has been reported as one of the reasons for NEETs' failure in the labor market.

• Journal of Information Technology Services
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• v.21 no.2
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• pp.1-26
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• 2022

This study classified five types of online food subscription economy: replenishment, curation, surprise, membership, and visitation. An online survey was conducted with 314 customers who experienced 5 types of online subscription economy. This study selected the characteristics of the food subscription economy as convenience, perceived personalization, economic utility, and timeliness through previous studies. The effect of the four characteristics on perceived value (utilitarian and emotional) and the relationship between customer engagement and perceived value, which are dependent variables that have never been used in the food subscription economy, were verified through the S-O-R model. In this relationship, we demonstrated how consumers' personal tendencies, such as need for cognitive closure and self-efficacy, mediate between timeliness and perceived value related to online food delivery. The study results are as follows. Perceived personalization, convenience, and timeliness had a positive effect on the utilitarian value in the order. It also had a positive effect on emotional values in the order of perceived personalization and timeliness. On the other hand, economic utility had no significant effect on practical branches. Customer engagement had a positive effect in the order of emotional value and utilitarian value. The lower the need for cognitive closure the more positive the utilitarian value. The lower the self-efficacy, the more positive the emotional value was perceived. Through the above study, companies that want to operate or start an online food subscription economy need a strategic approach rather than unreasonable price discounts in pricing policy. In addition, it is necessary to focus on marketing activities that provide emotional value by focusing on perceived personalization, which is the satisfaction factor of online food subscription.

• Journal of the Korean Society for information Management
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• v.19 no.3
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• pp.123-143
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• 2002

Developing good methods for image description and indexing is fundamental for successful image retrieval, regardless of the content of images. Researchers and practitioners in the field of image indexing have developed a variety of image indexing systems and methods with the consideration of information types delivered by images. Such efforts in developing image indexing systems and methods include Panofsky's levels of image indexing and indexing systems adopting different approaches such as thesauri-based approach, classification approach. description element-based approach, and categorization approach. This study investigated users' perception of the objectiveness of image indexing, especially the iconographical analysis of image information advocated by Panofsky. One of the best examples of subjectiveness and conditional-dependence of image information is emotion. As a result, this study dealt with visual emotional information. Experiments were conducted in two phases : one was to measure the degree of agreement or disagreement about the emotional content of pictures among forty-eight participants and the other was to examine the inter-rater consistency defined as the degree of users' agreement on indexing. The results showed that the experiment participants made fairly subjective interpretation when they were viewing pictures. It was also found that the subjective interpretation made by the participants resulted from the individual differences in terms of their educational or cultural background. The study results emphasize the importance of developing new ways of indexing and/or searching for images, which can alleviate the limitations of access to images due to the subjective interpretation made by different users.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.3
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• pp.21-26
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• 2002

This paper presents the new approach of identifying human emotional states such as anger, happiness, normal, sadness, or surprise. This is accomplished by using discrete duration continuous hidden Markov models(DDCHMM). For this, the emotional feature parameters are first defined from input speech signals. In this study, we used prosodic parameters such as pitch signals, energy, and their each derivative, which were then trained by HMM for recognition. Speaker adapted emotional models based on maximum a posteriori(MAP) estimation were also considered for speaker adaptation. As results, the simulation performance showed that the recognition rates of vocal emotion gradually increased with an increase of adaptation sample number.

• Korean Journal of Childcare and Education
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• v.12 no.6
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• pp.79-102
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• 2016

The purpose of this study was to identify school adjustment groups by applying a Latent Profile Analysis(LPA) and to investigate the effects of children's emotional problems and study habits on determining the membership of these groups. LPA and multiple logistic regression were conducted using the data of 2,200 third-graders from the Korean Children and Youth Panel Study. The results are listed as follows. First, four school adjustment groups were identified: adjustment, approach to adjustment, maladjustment risk, and maladjustment group. Second, accomplishment value and mastery goal orientation were relatively strong predictors of membership of the school adjustment groups. Time management was also a significant variable that predicted the membership of maladjustment or the maladjustment-risk group. Third, attention problems and depression were the most consistent predictors of membership of maladjustment or the maladjustment-risk group. Physical symptoms and social withdrawal were also significant. Based on the results, implications for intervention to promote early school adjustment were discussed.

• KIISE Transactions on Computing Practices
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• v.23 no.6
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• pp.386-391
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• 2017

In this paper, we propose a system that automatically assigns user's experience-based emotion tags from wearable sensor data collected in real life. Four types of emotional tags are defined considering the user's own emotions and the information which the user sees and listens to. Based on the collected wearable sensor data from multiple sensors, we have trained a machine learning-based tagging system that combines the known auxiliary tools from the existing affective computing research and assigns emotional tags. In order to show the usefulness of this multi-modality-based emotion tagging system, quantitative and qualitative comparison with the existing single-modality-based emotion recognition approach are performed.

• The Research Journal of the Costume Culture
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• v.17 no.1
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• pp.1-14
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• 2009

The purpose of this study was to examine consumers' brand attachment of fashion luxury product by emotional approach of brand loyalty. The questionnaire developed through the literature search and a survey was conducted both in on-line and off-line questionnaire simultaneously. Finally, 218 data from women who had a buying experience of fashion luxury products were analyzed using frequency, factor analysis, ANOVA, t-test, regression analysis by SPSS for WIN program. The results were as follows; First, there were significant differences in brand loyalty according to consumers' age, marriage yes or no, occupation, education level, and income. Second, the consumers' brand attachment of fashion luxury products was composed of five factors; emotional, dependent, attractive, knowledge, interest. Third, there were significant differences in brand loyalty according to consumers' age, marriage yes or no, occupation, education level, and income. Fourth, there were significant differences in brand attachment according to consumers' age, marriage yes or no, occupation, education level, and income. There were significant differences in emotional, dependent, attractive, knowledge, interest dimension of brand attachment according to consumers' age, sex, marriage yes or no, occupation, education level, and income. Fifth, the brand attachment significantly influenced on the brand loyalty.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.1
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• pp.94-98
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• 2009

In this paper, we deal with a face recognition method for the emotional face images. Since the face recognition is one of the most natural and straightforward biometric methods, there have been various research works. However, most of them are focused on the expressionless face images and have had a very difficult problem if we consider the facial expression. In real situations, however, it is required to consider the emotional face images. Here, three basic human emotions such as happiness, sadness, and anger are investigated for the face recognition. And, this situation requires a robust face recognition algorithm then we use a fuzzy Fisher's Linear Discriminant (FLD) algorithm with the wavelet transform. The fuzzy Fisherface is a statistical method that maximizes the ratio of between-scatter matrix and within-scatter matrix and also handles the fuzzy class information. The experimental results obtained for the CBNU face databases reveal that the approach presented in this paper yields better recognition performance in comparison with the results obtained by other recognition methods.