• Journal of Venture Innovation
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• v.5 no.2
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• pp.67-84
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• 2022

This study attempts to verify the double mediating effects of trait anxiety and burnout in the relationship between socially prescribed perfectionism and subjective well-being of employees. For this study, 300 office workers were surveyed to measure the trait anxiety, the burnout, the socially prescribed perfectionism and subjective well-being. The collected data were analyzed with SPSS PROCESS Macroin an bootstrapping method. The result of this study can be summarized as follows. First, socially prescribed perfectionism had significant positive relationship with trait anxiety and burnout. Socially prescribed perfectionism had significant negative relationship with subjective well-being. Also trait anxiety had significant positive relationship with burnout and significant negative relationship with subjective well-being. In addition, burnout had significant negative relationship with subjective well-being. Second, trait anxiety and burnout fully mediated the relationship between socially prescribed perfectionism and subjective well-being. Third, there was a dual mediation effect on trait anxiety and burnout in the relationship between socially prescribed perfectionism and subjective well-being. In conclusion, it suggests that in order to elevate subjective well-being of employees with a high level of socially prescribed perfectionism, it would be more effective to deal with trait anxiety and burnout which are proved to make them happy than to deal with perfectionism itself.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.43-54
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• 2024

Recently, there has been a lot of research on utilizing Language Models, and Large Language Models have achieved innovative results in various tasks. However, the practical application faces limitations due to the constrained resources and costs required to utilize Large Language Models. Consequently, there has been recent attention towards methods to effectively utilize models within given resources. Curriculum Learning, a methodology that categorizes training data according to difficulty and learns sequentially, has been attracting attention, but it has the limitation that the method of measuring difficulty is complex or not universal. Therefore, in this study, we propose a methodology based on data heterogeneity-based Curriculum Learning that measures the difficulty of data using reliable prior information and facilitates easy utilization across various tasks. To evaluate the performance of the proposed methodology, experiments were conducted using 5,000 specialized documents in the field of information communication technology and 4,917 documents in the field of healthcare. The results confirm that the proposed methodology outperforms traditional fine-tuning in terms of classification accuracy in both LoRA fine-tuning and full fine-tuning.

• Journal of Distribution Science
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• v.7 no.3
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• pp.71-100
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• 2009

The main purpose of this study is to define relevant factors that influence successful start-ups and management innovations of traditional markets from the point of market structures and relations. To do this, we devide an entrepreneurship of merchant into two factors, risk taking and managerial experience and choose product planning and its implementation to see merchandising of traditional markets. In this study we identify that several factors we chose are contributing to generating management performances through market promotional parameters. Also we confirm that image factors of traditional markets is consist of awareness and value of markets, and that these factors shows some sequential and continual patterns in the course of generating performances. In additions, it is identified that four independent factors have positive effects to star-up success; risk taking 0.29(t 2.61), managerial experience 0.04(t 1.79), merchandising implementation 0.374(t 2.61), market value 0.47(t 5.25), market awareness 0.22(t 2.30). This study can help merchants of traditional markets to make and change their market strategies, restructure their businesses and survive in the field. This also provide some ideas and guidances to relevant government agencies in formulating traditional market policies.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.8
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• pp.34-41
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• 2005

A parallel computing technique was applied to large scale structure analysis or aerodynamic design and it is a essential element in reducing the huge computation time for large scale design problem. We can use a many computers for reducing the analysis time of multidisciplinary design optimization. But previous MDO frameworks can not support a parallel design process technique so still existing which calls an analysis program continuously. In this paper, We developed a MDO framework(MLR) which supports a parallel design process to solve sequential analysis call. Finally, three sample cases are presented to show the efficiency of design time using the suggested MDO framework.

• Human Ecology Research
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• v.62 no.1
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• pp.101-120
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• 2024

This study aimed to examine the effect of adolescents' perceived parental blame (criticism) on learned helplessness and to examine whether maladaptive metacognitive beliefs and rumination sequentially mediate the relationship between parental blame and learned helplessness. The participants were 316 adolescents (Mean age=16.7, SD=0.75; 137 male, 179 female) attending grades 1st and 2nd in high school in South Korea. The participants were selected using a snowball sampling method, while the data was collected via an online self-report questionnaire. This survey was completed by the participants and analyzed using SPSS 28.0, Amos 26.0 (IBM Co., Armonk, NY), and PROCESS macro version 4.2 (Model 6; Hayes, 2022). The main results are summarized as follows. Firstly, the adolescents' perceived paternal and maternal blame indicated significant direct effects on learned helplessness. Secondly, rumination mediated the effect of paternal and maternal blame on learned helplessness. Lastly, paternal and maternal blame significantly affected learned helplessness through the sequential mediating effects of maladaptive metacognitive beliefs and rumination. This study elucidates the causal structure among the various factors influencing learned helplessness in adolescents, focusing on parental blame, maladaptive metacognitive beliefs, and rumination. Furthermore, considering the verified sequential mediating effects of maladaptive metacognitive beliefs and rumination in the relationship between adolescents' perceived parental blame and learned helplessness, these findings suggest that modifying maladaptive metacognitive beliefs may help to reduce learned helplessness among adolescents who perceive high levels of parental blame.

• Human Ecology Research
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• v.61 no.3
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• pp.401-414
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• 2023

This study examined the sequential mediation effects of academic achievement attribution and career decision-making self-efficacy on the effect of paternal and maternal helicopter parenting on high-school students' career preparation behavior. A total of 285 (119 male and 166 female) Korean high-school students in the second grade participated in the study. Research variables were measured using the Career Preparation Behavior Scale (Kim, 1997), Helicopter Parenting Scale (LeMoyne & Buchanan, 2011), Attribution Questionnaire (Weiner, 1979), and Career Decision-Making Self-Efficacy Scale-Short Form (Betz et al., 1996). To examine the sequential mediating effect, data analysis was performed using SPSS 29.0 and PROCESS MACRO (v4.2) Model 6. The results revealed no correlations between helicopter parenting and academic failure attribution. However, higher paternal and maternal helicopter parenting were found to indirectly reduce high-school students' career preparation behavior through lower internal academic success attribution (effort and ability) and higher external academic success attribution (task difficulty and luck), which reduced career decision-making self-efficacy. These findings can be employed to develop more effective intervention programs comprising career guidance for adolescents, which emphasizes the negative effect of helicopter parenting. This study expands the research field, as previous findings on helicopter parenting mostly focus on college students.

• Human Ecology Research
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• v.61 no.3
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• pp.459-474
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• 2023

This study aimed to examine the effect of parentification on the smartphone dependency of upper elementary school children in double-income families and to verify whether ambivalence over emotional expression and loneliness sequentially mediates the relationship between parentification and smartphone dependency. The participants were 311 upper-elementary school students (4th to 6th graders; 126 boys, 40.5%) in doubleincome households residing in Seoul, Gyeong-gi, and Incheon. The data were collected through an online self-report questionnaire completed by the participants and were analyzed using SPSS 26.0 and Mplus 8.7 software. The results can be summarized as follows. Firstly, the direct effect of parentification on the smartphone dependency of the children from double-income families was statistically insignificant. Secondly, ambivalence over emotional expression mediated the effect of parentification on smartphone dependency, while loneliness did not. Lastly, parentification influenced smartphone dependency through the sequential mediating channel of ambivalence over emotional expression and loneliness. In conclusion, these findings indicate that interventions for smartphone-overdependent children from double-income families should place emphasis on children's psychological difficulties attributed to parentification. Specifically, this study highlights the importance of alleviating the levels of ambivalence over emotional expression and loneliness to address the issue of children's smartphone dependency in double-income families, suggesting possible involvement and support at both household and societal levels.

• Human Ecology Research
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• v.62 no.3
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• pp.573-583
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• 2024

Smart devices have become an essential part of human life; however, excessive dependency on these devices has become a serious issue for both children and adults. In this study, a research model was developed which hypothesized that mothers' smartphone addiction influences children's media dependency, and that this effect is sequentially mediated by mothers' work-family strains and parental monitoring. To test this hypothesis, an analysis of data from the 13th Wave of Panel Study on Korean Children (PSKC), conducted in 2020, was performed. The data were collected from 726 children (359 boys, 367 girls) with an average age of 12.23 years (SD=.30). The analysis was conducted using SPSS 27.0 and Process MACRO Ver. 4.2. The results were as follows. Maternal smartphone addiction increased mothers' work-family strains, which in turn lowered the level of parental monitoring, which was associated with an increase in children's media dependency. Furthermore, mothers' smartphone addiction had a partial mediation effect on children's media dependency. In sum, this study revealed that the mothers' smartphone addiction increased children's media dependency, and this effect was sequentially mediated by an increase in mothers' work-family strains and a decrease in parental monitoring. This study verifies that mothers' smart device usage as well as their working environment has a significant effect on children's media dependency. Therefore, to increase parental monitoring of children's media usage, mothers' work-family strains need to be reduced.

• Journal of Venture Innovation
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• v.3 no.2
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• pp.129-145
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• 2020

The purpose of this study was to examine the effects of the job characteristics of the PR practitioner on flourishing and to identify the mediating effect of the meaning of work and job engagement. This study used a survey of 353 PR practitioners from PR firms and the hypothesis was verified by gathering the data of the job characteristics, the meaning of work, the job engagement and the flourishing by conducting hierarchical regression analysis and SPSS Process Macro bootstrapping analysis. The results showed that the job characteristics of the PR practitioner were found to influence the flourishing with the double-mediation effect, the meaning of the work and job engagement. The more PR practitioners regard their work affects their surroundings, the higher the degree of autonomy is, and the more they get feedbacks they engaged more, as they valued their job more thereby experience flourish more. Moreover, when PR practitioners valued their job more, they more engaged in their work and experience flourish. The meaning of work revealed to be the important factor to affect flourish regardless of the job characteristics and the job engagement so that gained the results that PR firms' effort to elevate the meaning of work of PR practitioners has is important. The study findings suggest that PR practitioners' flourishing is manageable in the organization by paying attention to oneself in the aspect of the organization, not leaving in individual areas. Limitaions and implications for future studies were discussed.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.