• Journal of Korean Society of Forest Science
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• v.101 no.4
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• pp.695-702
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• 2012

The chestnut management standard diagnostic table is developed and would be utilized in order that a manager raising chestnuts checks where the own level of management is and grasps current state of own for the purpose of planning aims and advancing toward a higher level. The developed 'Chestnut management standard diagnostic table' consisted of 3 first classified items, 19 second classified items and 2 product indicator items by the chestnut experts consultative meeting. A survey of 212 farmhouses in 4 major producing area was conducted. Except invalid survey of 53 farmers, 159 farmhouses interviewed were used in analysis. Total score was calculated with sum of each item's score. According to the survey results, average score is 68.0 and Buyeo received the highest score of 69.7 and Suncheon received the lowest score of 61.8 by regional groups. The higher the group in score, the better it is in output per hectare. But the property of 'the ratio of the best products in total products' does not show a statistical correlation. Generally the score of 'management-based evaluation indicator part' and 'management and sale capacity indicator part' in Suncheon was low because of many elderly people. In part of 'manufacturing technology indicator' as Environmentally-Friendly production is progressed in over 70% of four regions, when comes to a disease and insect pest control there are rarely farmhouses having a way of crop dusting.

• Journal of Agricultural Extension & Community Development
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• v.4 no.1
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• pp.201-209
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• 1997

The objective of this study were 1) to review concepts of user information satisfaction (UIS), 2) to analyze tools for measuring user information satisfaction, 3) to identify factors affecting information satisfaction. The study was carried out by the analysis of literatures related to information system. The construct of UIS has been operationalized in many different ways. Several studies employed single-item rating scales; such scales have been criticized as unreliable. Single-item scales also provide little information as to what the user finds dissatisfying(satisfying) and are thus of limited utility outside a research setting. Multiple-item UIS measures have become increasingly common. Generally, they are of two types. The first focuses on the information system product. With such diverse names as "system acceptance", "output quality", and "appreciation", these scales focus on the content of the information system and the manner in which the information is presented. The second type of multiple-item scale includes the organizational support for developing and maintaining the system as well as the system product itself. This type of instrument contains items concerned with training, documentation, development procedures, systems maintenance, etc., as well as items related to system content. Thus it provides an indicator of the overall quality of information services provided by an information service function. Generally, UIS measures have not been carefully validated. Recently, however, several rigorous attempts have been made to develop valid and reliable UIS measure.

• Journal of the Korea Society for Simulation
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• v.29 no.4
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• pp.65-72
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• 2020

The purpose of this study is to analyze the adequacy of product qualification criteria through Monte-Carlo simulation and association rule analysis. We first surveyed the similar procurement systems, then we simulated the bid situation that randomly generated several vendors participated in a bid, and they obtained the score according to the product qualification criteria's judgement area. Then, the company with the highest score will win the bid, and further analysis was performed in terms of performance indicator and satisfaction ratio. The results of this study can be summarized as follows; Although the items related to the credibility accounted for the largest number items, it did not affect the actual bid results. It was analyzed that it is desirable to increase the allocation points in the area of business status and technical capability review than the current one.

• Korean Journal of Agricultural Science
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• v.37 no.3
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• pp.515-522
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• 2010

The purpose of this research was to construct an efficient management system in developing and supplying a 'management standard diagnostic table' for the improvement of chestnut cultivation farmhouse. 'Chestnut management standard diagnostic table' were based from the actual condition of chestnut management in Chungcheongnam-do, selected 'appraisal factor item and by consulting 'agricultural plant standard diagnostic table' and various kinds of data which had already been developed. This research also consulted the classification systems and degree of importance. The developed 'Chestnut management diagnostic table' consisted of 3 first classified items, 19 second classified items and 2 product indicator items.

• Korean Journal of Agricultural Science
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• v.48 no.3
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• pp.473-482
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• 2021

In this study, we attempted to estimate the degree of management of chestnut forestry households in Chungcheongnam-do and to provide information for establishing chestnut cultivation-related policies. The chestnut management standard diagnostic table consists of three major categories, namely, management base, management and sales capacity, and production technology levels, along with 19 subcategories. A survey of 309 chestnut forestry households was conducted from 2014 to 2019 in Gongju, Cheongyang, and Buyeo in Chungcheongnam-do. The average score for the 19 subcategories was 65.7 points, indicating that these areas have excellent management conditions. When the total score was higher, the output per hectare and the rate of top-grade products in the total output were also higher, indicating a significant correlation. These findings will be useful for providing consulting services to chestnut growers as they highlight the correlation between the higher scores of the indicators in the chestnut management standard diagnostic table and the management performance of the farmers. We found that the scores of the indicators for management and sale skill, such as management record and analysis, material purchase, and direct transaction with consumers, were relatively lower than those of the indicators for management base and production skill. It is assumed that the chestnut growers aging has led to negligence in recording details on incomes, expenditures, and work and lowered the willingness to make substantial profits. Therefore, it is essential to overcome these problems for profitable chestnut farming.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.27-42
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• 2020

Traditional companies with offline stores were unable to secure large display space due to the problems of cost. This limitation inevitably allowed limited kinds of products to be displayed on the shelves, which resulted in consumers being deprived of the opportunity to experience various items. Taking advantage of the virtual space called the Internet, online shopping goes beyond the limits of limitations in physical space of offline shopping and is now able to display numerous products on web pages that can satisfy consumers with a variety of needs. Paradoxically, however, this can also cause consumers to experience the difficulty of comparing and evaluating too many alternatives in their purchase decision-making process. As an effort to address this side effect, various kinds of consumer's purchase decision support systems have been studied, such as keyword-based item search service and recommender systems. These systems can reduce search time for items, prevent consumer from leaving while browsing, and contribute to the seller's increased sales. Among those systems, recommender systems based on association rule mining techniques can effectively detect interrelated products from transaction data such as orders. The association between products obtained by statistical analysis provides clues to predicting how interested consumers will be in another product. However, since its algorithm is based on the number of transactions, products not sold enough so far in the early days of launch may not be included in the list of recommendations even though they are highly likely to be sold. Such missing items may not have sufficient opportunities to be exposed to consumers to record sufficient sales, and then fall into a vicious cycle of a vicious cycle of declining sales and omission in the recommendation list. This situation is an inevitable outcome in situations in which recommendations are made based on past transaction histories, rather than on determining potential future sales possibilities. This study started with the idea that reflecting the means by which this potential possibility can be identified indirectly would help to select highly recommended products. In the light of the fact that the attributes of a product affect the consumer's purchasing decisions, this study was conducted to reflect them in the recommender systems. In other words, consumers who visit a product page have shown interest in the attributes of the product and would be also interested in other products with the same attributes. On such assumption, based on these attributes, the recommender system can select recommended products that can show a higher acceptance rate. Given that a category is one of the main attributes of a product, it can be a good indicator of not only direct associations between two items but also potential associations that have yet to be revealed. Based on this idea, the study devised a recommender system that reflects not only associations between products but also categories. Through regression analysis, two kinds of associations were combined to form a model that could predict the hit rate of recommendation. To evaluate the performance of the proposed model, another regression model was also developed based only on associations between products. Comparative experiments were designed to be similar to the environment in which products are actually recommended in online shopping malls. First, the association rules for all possible combinations of antecedent and consequent items were generated from the order data. Then, hit rates for each of the associated rules were predicted from the support and confidence that are calculated by each of the models. The comparative experiments using order data collected from an online shopping mall show that the recommendation accuracy can be improved by further reflecting not only the association between products but also categories in the recommendation of related products. The proposed model showed a 2 to 3 percent improvement in hit rates compared to the existing model. From a practical point of view, it is expected to have a positive effect on improving consumers' purchasing satisfaction and increasing sellers' sales.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.53 no.6
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• pp.851-860
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• 2020

Shrimp in seasoned soy sauce (S-SS) is a popular seafood product in Korea, but it could be potentially hazardous; thus, this study was conducted to investigate its safety. Commercial S-SS were collected and analyzed for pH, volatile basic nitrogen (VBN), hygenic indicator microorganisms (viable cell count, coliforms, and Escherichia coli), food poisoning bacteria (Staphylococcus aureus, Vibrio parahaemolyticus, and Listeria monocytogenes), preservatives (dehydroacetic acid, sorbic acid, benzoic acid, and its salt, parahydroxybenzoate), tar colorants, and sensory properties. Domestic and foreign standards were also investigated for S-SS. Commercial S-SS ranged from to 6.2-7.3 for pH, 13.7-39.1 mg/100 g for VBN, and 4.6-6.9 log CFU/g for viable cells. The coliforms and E. coli of commercial S-SS were from ND to 3.4 log CFU/g and negative, respectively. Food poisoning bacteria, preservatives, and tar colorants were not detected in commercial S-SS. Only the coliform count and presence of E. coli in commercial S-SS exceeded the set standards of vietnam, while all items were within domestic and foreign standards.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.67-86
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• 2020

Recommender Systems have been huge influence users and business more and more. Recently the importance of E-commerce has been reached rapid growth greatly in world-wide COVID-19 pandemic. Recommender system is the center of E-commerce lively. Top ranked E-commerce managers mentioned that recommender systems have a major influence on customer's purchase such as about 50% of Netflix, Amazon sales from their recommender systems. Most algorithms have been focused on improving accuracy of recommender system regardless of novelty, diversity, serendipity etc. Recommender systems with only high accuracy cannot satisfy business long-term profit because of generating sales polarization. In addition, customers do not experience enjoyment of shopping from only focusing accuracy recommender system because customer's preference is changed constantly. Therefore, recommender systems with various values need to be developed for user's high satisfaction. Reranking is the most useful methodology to realize diversity of recommender system. In this paper, diversity of recommender system is represented through constructing high similarity with users who have different preference using each user's purchased item's category algorithm. It is distinguished from past research approach which is changing the algorithm of recommender system without user's diversity preference level. We tried to discover user's diversity preference level and observed the results how the effect was different according to user's diversity preference level. In addition, graph-based recommender system was used to show diversity through user's network, not collaborative filtering. In this paper, Amazon Grocery and Gourmet Food data was used because the low-involvement product, such as habitual product, foods, low-priced goods etc., had high probability to show customer's diversity. First, a bipartite graph with users and items simultaneously is constructed to make graph-based recommender system. However, each users and items unipartite graph also need to be established to show diversity of recommender system. The weight of each unipartite graph has played crucial role changing Jaccard Distance of item's category. We can observe two important results from the user's unipartite network. First, the user's diversity preference level is observed from the network and second, dissimilar users can be discovered in the user's network. Through the research process, diversity of recommender system is presented highly with small accuracy loss and optimalization for higher accuracy is possible controlling diversity ratio. This paper has three important theoretical points. First, this research expands recommender system research for user's satisfaction with various values. Second, the graph-based recommender system is developed newly. Third, the evaluation indicator of diversity is made for diversity. In addition, recommender systems are useful for corporate profit practically and this paper has contribution on business closely. Above all, business long-term profit can be improved using recommender system with diversity and the recommender system can provide right service according to user's diversity level. Lastly, the corporate selling low-involvement products have great effect based on the results.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.97-117
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• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.