• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.27 no.2
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• pp.63-70
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• 2021

As the government strengthens its policy of separating and discharging packaging materials, consumers are increasingly dissatisfied. In order to increase consumer participation in separate discharge policy of packaging materials, it is necessary to increase the willingness to participate by reducing potential consumer problems such as removal of packaging labels. This study conducted a survey of 300 consumers aged 14 and over who recycle and discharge directly from their homes. Ninety-nine percent of consumers said PET bottles are released separately. However, only 65% of consumers removed labels (attachment labels, shrink labels) and other materials (caps, vinyl coatings, tapes, handles, bases, etc.) during separate discharge process. Nearly 52% of consumers cited 'difficulty of separation' as the main reason for not removing labels and other materials. One-way ANOVA analysis showed that 'strong adhesion', 'removal initiation problem' and 'material strength' had high mean regardless of age, which are major factors impedes label removal. Using shrink labels with perforated lines rather than adhesive labels would be more beneficial to encouraging participation in separate discharge. However, if the shrink labels do not have perforated lines or are difficult to remove, adhesive labels are often easier to remove than shrink labels because of the strong cohesiveness of shrink labels. As a result, how easy it is for consumers to remove the label is more important than technological differences. In order to increase consumer participation in packaging material and label separations, improvements in structural design are needed along with the selection of materials that are easy to separate. This study is meaningful in examining consumer perceptions, deriving problems and suggesting directions for policy improvement.

• Journal of Nutrition and Health
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• v.24 no.3
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• pp.230-242
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• 1991

This research was conducted on the 10 take-out boxed-lunches commercially prepared in the department stores. chain stores. and the public railroad trains in Korea. Sampling was conducted from February 1990 to March 1990. Nutritive values and sodium contents of the 10 boxed-lunch samples are summarized as follows : 1) The average weight(percentage) of the cooked rice and the side dishes were 304.6g(49.4) and 312.4(506%), respectively. The weight of these samples were significantly heavier than that of Japanese style boxed-lunches. 2) The average number of the side dishes was 12. The average numbers of food items classified by the five food groups were 6.1 in protein food group, 0.3 in calcium food group. 6.0 in vitamin and mineral food group. 1.5 in carbohydrate food group, and 1.5 in oil and fat food group. 3) They contained on the average 840.7kcal of energy, 38.9g of protein, 22.7g of fat, 120.4g of carbohydrate. 300.8mg of calcium. 410.8mg of phosphours, 6.61 mg of iron. 219.8 R.E. of vitamin A, 0.46mg of thiamin, 0.67mg of riboflavin, 10.5mg of niacin, 27.5mg of ascorbic acid. Thus. except vitamin t the content of all the nutrients were higher than the value of 1/3 of the RDA for adults. 4) The high priced group(group 2) had more protein, calcuim. iron and niacin contents than the cheaper group(group 1). Probably, it's because the group 2 had more animal foods than the group 1. 5) The average energy content per unit price(100 won) was 37.3kcal and the average protein content per unit price(100 won) was 1.64g. Korena style boxed-lunches had higher energy and protein contents per unit price than Japanese style, and the group 1 higher than the group 2. 6) The average energy Proportions of Protein, carbohydrate. and fat were 18.3%, 57.4%, and 24.3%, respectively. These proportions are good enough. 7) Frequency of cooking methods for the side dishes were found in the decreasing order : pan-frying, frying, braising, seasoning, kimchi, grilling, pickling, stir-frying, steaming and fermenting. Generally simple cooking methods were used, thus the menus were lack or varieties. 8) Frequency of colors for the side dishes were found in the decreasing order : red, brown. yellow, green, black, white. Too much red pepper was used. 9) The average capacity of the containers for the staples and the side dishes were 468.1ml and 590.6ml, respectively. And the containers could not keep the food items well seperated. 10) The average contensts of sodium and salt were 2.287mg and 5.76g, in the range of 1, 398mg to 3, 489mg and 3.53g to 8.80g, respectively. These are much higher values than the recommended amount of salt.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.