• The Journal of Asian Finance, Economics and Business
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• v.6 no.3
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• pp.193-203
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• 2019

The issue of health risks from consuming unhealthy product is an important issue that is happening right now. Both developed and developing countries are already aware of the need for attention to the health-risk products. One tool that is believed to be able to change the consumption behavior of the health-risk products is the use of warning label on product packaging. As a persuasive act, both visual and textual warning label are believed to be able to change people's consumption behavior. In addition to the labels that contain health hazards, this research also uses social consequence contents. The main targets of such unhealthy product marketing are children and adolescents. Correspondingly, this study targets the age groups of kids and tweens. The method used in this research is experiment, involving 180 participants from two age groups namely kids and tweens. As a result, the study found that the influence of warning label on the age of tweens is greater in the age of the children. Meanwhile, the use of visual and textual warning label using social consequences contents, proved to be effective at the age of tweens. These results are useful for enrich social marketing subjects, especially within warning label research.

• Journal of the Korean Home Economics Association
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• v.35 no.1
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• pp.47-58
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• 1997

The purpose of this study was to investigate the relationship between children's gender role attitude measured by component model and social competency. Subjects were 232 elementary school children: 113 4th graders and 119 6th graders. The main results were as follows. First there were significant differences in mean scores of gender role attitude with age and sex variable. And among three dimensions of component model(i.e, gender label-component links within-component links between-component links) the difference was most discriminant in gender label-component links. Second there were significant relationship between gender role attitude and social competency. Among three dimensions of gender role attitude the most predictor variable for social competency was gender label-component links. And among four dimensions of social competency the most effective criterion variable for gender role attitude was leadership. And there were significant differences in social competency score with mother's educational level and sex of children as well as gender role attitude. In conclusion children's gender role attitude influence their social competency. More flexible gender role attitude they have more improved in their social competency , especially in boys.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.1063-1070
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• 2021

The study aims to investigate the role of consumer's social risk perceptions in retailing private label brands. Since private label brands are exclusively available at retail stores, consumers make their purchase decisions regarding them based on the image of that retail outlet. While buying them, risk perceptions are influenced by the retail store's image. The study identifies various retail store dimensions. For this purpose, primary data was collected using a survey questionnaire that was administered to a representative sample of retail store consumers in Riyadh. The data was analyzed and exploratory factor analysis was applied using SPSS 25 version to extract store image dimensions. The results showed six significant dimensions of retail store image namely 'Sales Staff', 'Promotion', 'Store Environment', 'Store Services', 'Product Assortment', and 'Customer Convenience'. Regression Analysis was performed and the effect of these retail store image dimensions was tested on social risk perceptions of consumers. Results indicate that store image dimensions significantly influence consumer's perceived social risk perceptions. However, the relationship is not consistent across all the six identified store image dimensions. The study brings forth several valuable consumer insights and the findings of the study have some very interesting and practical implications for retailers.

• The Research Journal of the Costume Culture
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• v.23 no.3
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• pp.523-536
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• 2015

Corporate association-which refers to consumers' beliefs, knowledge, perceptions, and evaluations of a corporation -can affect consumers' purchasing intentions. Corporate association consists of corporate ability association and corporate social responsibility association. Corporate ability association refers to a company's product quality, corporate innovation, productivity, consumer orientation, and after service. Corporate social responsibility association, which refers to the social perspective a company has of its responsibility to society, can affect corporate image and consumers' purchasing intentions. Eco-labeling for protecting and sustaining the environment is one of the important green marketing strategies in the fashion business that can influence corporate association and consumers' purchasing intentions. The purpose of this study was to investigate the effect of consumers' eco-label recognition on their corporate association and intentions to purchase eco-friendly fashion products. Questionnaires were distributed to consumers. The 263 usable questionnaires that were returned were analyzed by descriptive statistics, Cronbach's alpha, factor analysis, regression analysis, and t-test. The results were as follows: There was a significant effect of eco-label recognition on corporate association (ability association and social responsibility association). Eco-label recognition and corporate association were found to significantly affect consumers' purchasing intentions. Regarding the eco-friendly fashion product buying experience, there was no significant difference on corporate association and buying intention, but there was significant difference on eco-label recognition.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.4
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• pp.709-716
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• 2020

In social media, when posting a post, tag information of an image is generally used because the search is mainly performed using a tag. Users want to expose the post to many people by attaching the tag to the post. Also, the user has trouble posting the tag to be tagged along with the post, and posts that have not been tagged are also posted. In this paper, we propose a method to find an image similar to the input image, extract the label attached to the image, find the posts on instagram, where the label exists as a tag, and recommend other tags in the post. In the proposed method, the label is extracted from the image through the model of the convolutional neural network (CNN) deep learning technique, and the instagram is crawled with the extracted label to sort and recommended tags other than the label. We can see that it is easy to post an image using the recommended tag, increase the exposure of the search, and derive high accuracy due to fewer search errors.

• Korean Journal of Human Ecology
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• v.20 no.1
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• pp.143-154
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• 2011

The purpose of this study was to examine consumers' ability to distinguish advertorial and editorial about consumer information. The data were collected between June, 28 and July, 2 in 2010 through the Internet surveys. Total of 603 respondents were included in the analysis. The findings are follows as: First, consumers showed better ability to discern advertorial than ability to discern editorial. It implied that editorials could be considered as advertorial rather than advertorial could be considered as editorials. Second, it seems likely that rather than executional cues such as format and source information, the types of products/services were used as source cues among consumers. Third, consumers tend to prefer 'consumer information', 'advertorial' and 'advertisement' among 10 given advertising labels. In Conclusion, to prevent misleading potentials of advertorial and editorials, standardized advertising label should be used and notify consumers of advertising label.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.273-276
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• 2019

There is a lot of information in our world, quick access to the most accurate information or finding the information we need is more difficult and complicated. The recommendation system has become important for users to quickly find the product according to user's preference. A social recommendation system using community detection based on label propagation is proposed. In this paper, we applied community detection based on label propagation and collaborative filtering in the movie recommendation system. We implement with MovieLens dataset, the users will be clustering to the community by using label propagation algorithm, Our proposed algorithm will be recommended movie with finding the most similar community to the new user according to the personal propensity of users. Mean Absolute Error (MAE) is used to shown efficient of our proposed method.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.29-41
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• 2021

Recently, there are increasing attempts to utilize deep learning methodology in the fashion industry. Accordingly, research dealing with various fashion-related problems have been proposed, and superior performances have been achieved. However, the studies for fashion style classification have not reflected the characteristics of the fashion style that one outfit can include multiple styles simultaneously. Therefore, we aim to solve the multi-label classification problem by utilizing the dependencies between the styles. A multi-label recognition model based on a graph convolution network is applied to detect and explore fashion styles' dependencies. Furthermore, we accelerate model training and improve the model's performance through transfer learning. The proposed model was verified by a dataset collected from social network services and outperformed baselines.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3935-3949
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• 2021

With the rapid development of Internet and big data technology, various online social network platforms have been established, producing massive information every day. Hot topic discovery aims to dig out meaningful content that users commonly concern about from the massive information on the Internet. Most of the existing hot topic discovery methods focus on a single network data source, and can hardly grasp hot spots as a whole, nor meet the challenges of text sparsity and topic hotness evaluation in cross-network scenarios. This paper proposes a novel hot topic discovery method across social network based on an im-proved LDA model, which first integrates the text information from multiple social network platforms into a unified data set, then obtains the potential topic distribution in the text through the improved LDA model. Finally, it adopts a heat evaluation method based on the word frequency of topic label words to take the latent topic with the highest heat value as a hot topic. This paper obtains data from the online social networks and constructs a cross-network topic discovery data set. The experimental results demonstrate the superiority of the proposed method compared to baseline methods.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.91-99
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• 2019

Existing node-to-node based optimal path searching is built on the assumption that all destination nodes can be arrived at from an origin node. However, the recent appearance of the adaptive path search algorithm has meant that the optimal path solution cannot be derived in node-to-node path search. In order to reflect transportation data at the links in real-time, the necessity of the node-to-link (or link-to-node; NL) problem is being recognized. This research assumes existence of a network with link-label and non-additive path costs as a solution to the node-to-link optimal path problem. At the intersections in which the link-label has a turn penalty, the network retains its shape. Non-additive path cost requires that M-similar paths be enumerated so that the ideal path can be ascertained. In this, the research proposes direction deletion and turn restriction so that regulation of the loop in the link-label entry-link-based network transformation method will ensure that an optimal solution is derived up until the final link. Using this method on a case study shows that the proposed method derives the optimal solution through learning. The research concludes by bringing to light the necessity of verification in large-scale networks.