• Journal of Korean Dental Hygiene Science
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• v.6 no.2
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• pp.1-12
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• 2023

Objectives: This study attempted to identify the eating habits and oral health behaviors of Korean teenagers, studying their relationship with oral health status. The findings serve as fundamental data to enhance proper eating habits and oral health-related projects, ultimately improving teenagers' oral health. Methods: It was analyzed through the original data of the 16th (2020) online survey of youth health behavior in Korea, Frequency analysis, complex sample cross-analysis, complex sample logistic regression analysis were conducted using the SPSSwin 21.0 program. Results: As a result of the study, was associated with the consumption of all sweet drinks, fast food intake, and the frequency of daily brushing over the past 7 days Teeth pain is noted with the consumption of soda, sweet drinks, fast food, and the frequency of daily brushing over the past 7 days. Gum bleeding is noted with the consumption of sweetened products, fast food intake, and the frequency of daily brushing over the past 7 days. Conclusions: Eating habits and oral health behaviors should be considered for the oral health management of middle and high school students. Specific measures should be sought to provide proper dietary education and systematic oral health education to improve the oral health of middle and high school students.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.525-532
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• 2024

This study explores the impact of physical characteristics (e.g., shape, color, material, size, weight, technical features) of smart wristbands and smartwatches on consumers' perceived functional, aesthetic, and symbolic values using an extended technology acceptance model. An online survey was conducted with adult residents of the United States who had experience using smart wristbands or smartwatches. Participants were asked about various physical characteristics of products they had used in the past year or were currently using, and their evaluations of these characteristics. The results revealed that the shape of the front display shape significantly influenced symbolic value, with circle shape and square shpae showing significantly higher symbolic value than rectangle shape. Wristband materials also had a significant impact on symbolic value, with metal and leather showing higher symbolic value among various materials. Additionally, an increase in product size was associated with higher symbolic value. Moreover, certain technical features such as activity tracker, alarm clock, and distance tracking influenced perceived functional value, while functions like time display, GPS, and email influenced perceived aesthetic value. Pedometer, GPS, and email were found to enhance perceived symbolic value. These findings provide valuable insights into consumer preferences for smart wristbands and smartwatches, serving as valuable information for product improvement and new product development.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.3
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• pp.97-107
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• 2024

This study aims to identify the factors that affect customer satisfaction as the market growth of rental subscription services for living environment home appliances increases. Unlike previous research, which focused on online subscriptions (e.g., digital content, over-the-top (OTT) services, e-books, and mobile devices), this study expands the scope to include rental subscriptions for household environmental appliances. Specifically, this study analyzes the factors influencing customer satisfaction among rental subscription service users by examining the effects of perceived usefulness, reliability, and convenience. The results show that users' perceived reliability and convenience of rental subscription services for living environment home appliances significantly affect customer satisfaction. Perceived usefulness, however, was not found to have a significant impact, as it is an abstract and subjective customer aspect. The implications of the results are as follows: First, standardized services must be strengthened to increase the reliability of rental subscription services. Additionally, it is necessary to improve convenience by developing additional services when managing regular visits tailored to the characteristics of each product. Providing customized services by integrating products and Information and Communications Technologies (ICT). Furthermore, effective customer management to increase customer satisfaction is crucial, as it can lead to cross-selling and up-selling opportunities. Lastly, venture start-ups should actively apply a subscription service business model.

• Journal of Nutrition and Health
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• v.57 no.4
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• pp.435-450
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• 2024

Purpose: This study developed the evaluation components and criteria for the Korean Healthy Diet (KHD) and assessed the current compliance of Koreans. Methods: The study reviewed domestic and international dietary guidelines and literature and conducted an online survey of 514 Korean adults to understand their nutritional perceptions, specifically the perceived importance of health and incorporation into usual diet. Data from the Korea National Health and Nutrition Examination Survey (KNHANES) were used to investigate food and nutrient intake patterns and examine the relationship between intake and metabolic syndrome (MetS). Based on these data, the components and criteria for a KHD were established by sex and age, and adherence was assessed. Results: The KHD evaluation included 13 dietary components: carbohydrates, sugar, fiber, protein, total fat, saturated fat, sodium, calcium, mixed grains, meat·fish·eggs·beans, vegetables, fruits, and dairy products. Applying the selected components and criteria to data from the KNHANES (2019-2021), the average KHD adherence score for Korean adults was 5.465 ± 0.023 out of a maximum score of 13. The score significantly increased with age (4.766 ± 0.044 for 19-29 years; 5.276±0.032 for 30-49 years; 6.109 ± 0.033 for 50-64 years), and women (5.642 ± 0.028) had higher scores than men (5.284 ± 0.030) (p < 0.05). Furthermore, the total score significantly differed between those with MetS (5.518 ± 0.045) and those without (5.568 ± 0.026) after adjusted for sex and age (p < 0.05). When scoring the dietary components, sugar (0.852 ± 0.004) and proteins (0.881 ± 0.004) scored relatively higher in the association with MetS, whereas calcium (0.148 ± 0.004) and mixed grains (0.225 ± 0.005) scored relatively lower. Conclusions: The KHD evaluation criteria could be used as a tool for screening and monitoring the overall diet quality of Koreans.

• Information Systems Review
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• v.26 no.1
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• pp.385-407
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• 2024

This study explains consumers' showrooming which refers to the activities of visiting offline stores to check products in person and obtaining information offline and online via mobile devices before making the final decision to buy. More specifically, this study verifies key determinants of showrooming based on two dimensions of the mobile technology and user characteristics. Furthermore, the study examines the relationship of showrooming and purchase intentions and the moderating effect of perceived risks on the relationship. The key findings are as follows: firstly, service connectivity and time convenience of the mobile technology characteristics are positively related to showroming. Secondly, as the user characteristics, need for touch and personal innovativeness increase showrooming while impulsiveness does not. Thirdly, showrooming contributes to the increase of purchase intentions. Finally, moderating effect of perceived risks has turned out to be insignificant. This study has implications by providing the understanding of key determinants of showrooming and further proving the positive relationship of showrooming and purchase intentions.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.129-142
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• 2016

Customer product reviews have become one of the important factors for purchase decision makings. Customers believe that reviews written by others who have already had an experience with the product offer more reliable information than that provided by sellers. However, there are too many products and reviews, the advantage of e-commerce can be overwhelmed by increasing search costs. Reading all of the reviews to find out the pros and cons of a certain product can be exhausting. To help users find the most useful information about products without much difficulty, e-commerce companies try to provide various ways for customers to write and rate product reviews. To assist potential customers, online stores have devised various ways to provide useful customer reviews. Different methods have been developed to classify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most shopping websites provide customer reviews and offer the following information: the average preference of a product, the number of customers who have participated in preference voting, and preference distribution. Most information on the helpfulness of product reviews is collected through a voting system. Amazon.com asks customers whether a review on a certain product is helpful, and it places the most helpful favorable and the most helpful critical review at the top of the list of product reviews. Some companies also predict the usefulness of a review based on certain attributes including length, author(s), and the words used, publishing only reviews that are likely to be useful. Text mining approaches have been used for classifying useful reviews in advance. To apply a text mining approach based on all reviews for a product, we need to build a term-document matrix. We have to extract all words from reviews and build a matrix with the number of occurrences of a term in a review. Since there are many reviews, the size of term-document matrix is so large. It caused difficulties to apply text mining algorithms with the large term-document matrix. Thus, researchers need to delete some terms in terms of sparsity since sparse words have little effects on classifications or predictions. The purpose of this study is to suggest a better way of building term-document matrix by deleting useless terms for review classification. In this study, we propose neutrality index to select words to be deleted. Many words still appear in both classifications - useful and not useful - and these words have little or negative effects on classification performances. Thus, we defined these words as neutral terms and deleted neutral terms which are appeared in both classifications similarly. After deleting sparse words, we selected words to be deleted in terms of neutrality. We tested our approach with Amazon.com's review data from five different product categories: Cellphones & Accessories, Movies & TV program, Automotive, CDs & Vinyl, Clothing, Shoes & Jewelry. We used reviews which got greater than four votes by users and 60% of the ratio of useful votes among total votes is the threshold to classify useful and not-useful reviews. We randomly selected 1,500 useful reviews and 1,500 not-useful reviews for each product category. And then we applied Information Gain and Support Vector Machine algorithms to classify the reviews and compared the classification performances in terms of precision, recall, and F-measure. Though the performances vary according to product categories and data sets, deleting terms with sparsity and neutrality showed the best performances in terms of F-measure for the two classification algorithms. However, deleting terms with sparsity only showed the best performances in terms of Recall for Information Gain and using all terms showed the best performances in terms of precision for SVM. Thus, it needs to be careful for selecting term deleting methods and classification algorithms based on data sets.

• Korean Journal of Food Science and Technology
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• v.52 no.1
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• pp.1-10
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• 2020

The present study identified the restrictions on the use of sous-vide products in the Korean HMR market for small and medium-sized manufacturing companies. A detailed literature review revealed that the HMR market in Korea is close to saturation. Notably, the technologically advanced products produced using sous-vide seem to display significant potential to overcome market saturation. The sous-vide method differs from conventional cooking techniques and is characterized by maintenance of food texture along with flavor enhancement. However, due to the unfamiliarity of the manufacturers with this method and the unclear food safety regulations, mass food manufacturing companies do not agree on using this method; hence, sous-vide production is usually undertaken by small/medium sized companies catering primarily through online marketing portals. This study highlights the various restrictions to the implementation of sous-vide production, and discusses several practical implications of sous-vide production that would help users of this technique enter the HMR market.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• Asia pacific journal of information systems
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• v.23 no.2
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• pp.1-20
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• 2013

Social commerce has been gaining explosive popularity, with typical examples of the model such as Groupon and Level Up. Both local business owners and consumers can benefit from this new e-commerce model. Local business owners have a chance to access potential customers and promote their products in a way that could not have otherwise been easily possible, and consumers can enjoy discounted offerings. However, questions have been increasingly raised about the value and future of the social commerce model. A recent survey shows that about a third of 324 business owners who ran a daily-deal promotion in Groupon went behind. Furthermore, more than half of the surveyed merchants did not express enthusiasm about running the promotion again. The same goes for the case in Korea, where more than half of the surveyed clients reported no significant change or even decrease in profits compared to before the use of social commerce model. Why do local business owners fail to exploit the benefits from the promotions and advertisements through the social commerce model and to make profits? Without answering this question, the model would fall under suspicion and even its sustainability might be challenged. This study aims to look into problems in the current social commerce transactions and provide implications for the social commerce model, so that the model would get a foothold for next growth. Drawing on justice theory, this study develops theoretical arguments for the effects of transaction characteristics on consumers' distributive justice and purchase intention in the social commerce. Specifically, this study focuses on two characteristics of social commerce transactions-the discount rate and the purchase rate of products-and investigates their effects on consumers' perception of distributive justice for discounted transactions in the social commerce and their perception of distributive justice for regular-priced transactions. This study also examines the relationship between distributive justice and purchase intention. We conducted an online experiment and gathered data from 115 participants to test the hypotheses. Each participant was randomly assigned to one of nine manipulated scenarios of social commerce transactions, which were generated based on the combination of three levels of purchase rate (high, medium, and low) and three levels of discount rate (high, medium, and low). We conducted MANOVA and post-hoc ANOVA to test hypotheses about the relationships between the transaction characteristics (purchase rate and discount rate) and distributive justice for each of the discounted transaction and the regular-priced transaction. We also employed a PLS analysis to test relations between distributive justice and purchase intentions. Analysis results show that a higher discount rate increases distributive justice for the discounted transaction but decreases distributive justice for the regular-priced transaction. This, coupled with the result that distributive justice for each type of transaction has a positive effect on the corresponding purchase intention, implies that a large discount in the social commerce may be helpful for attracting consumers, but harmful to the business after the promotion. However, further examination reveals curvilinear effects of the discount rate on both types of distributive justice. Specifically, we find distributive justice for the discounted transaction increases concavely as the discount rate increases while distributive justice for the regular-priced transaction decreases concavely with the dscount rate. This implies that there exists an appropriate discount rate which could promote the discounted transaction while not hurting future business of regular-priced transactions. Next, the purchase rate is found to be a critical factor that facilitates the regular-priced transaction. It has a convexly positive influence on distributive justice for the transaction. Therefore, an increase of the rate beyond some threshold would lead to a substantial level of distributive justice for the regular-priced transaction, threrby boosting future transactions. This implies that social commerce firms and sellers should employ various non-price stimuli to promote the purchase rate. Finally, we find no significant relationship between the purchase rate and distributive justice for the discounted transaction. Based on the above results, we provide several implications with future research directions.