• Asia pacific journal of information systems
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• v.22 no.1
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• pp.29-52
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• 2012

Consumers differ in the way they make a purchase. An audio mania would willingly make a bold, yet serious, decision to buy a top-of-the-line home theater system, while he is not interested in replacing his two-decade-old shabby car. On the contrary, an automobile enthusiast wouldn't mind spending forty thousand dollars to buy a new Jaguar convertible, yet cares little about his junky component system. It is product involvement that helps us explain such differences among individuals in the purchase style. Product involvement refers to the extent to which a product is perceived to be important to a consumer (Zaichkowsky, 2001). Product involvement is an important factor that strongly influences consumer's purchase decision-making process, and thus has been of prime interest to consumer behavior researchers. Furthermore, researchers found that involvement is closely related to perceived risk (Dholakia, 2001). While abundant research exists addressing how product involvement relates to overall perceived risk, little attention has been paid to the relationship between involvement and different types of perceived risk in an electronic commerce setting. Given that perceived risk can be a substantial barrier to the online purchase (Jarvenpaa, 2000), research addressing such an issue will offer useful implications on what specific types of perceived risk an online firm should focus on mitigating if it is to increase sales to a fullest potential. Meanwhile, past research has focused on such consumer responses as information search and dissemination as a consequence of involvement, neglecting other behavioral responses like online merchant selection. For one example, will a consumer seriously considering the purchase of a pricey Guzzi bag perceive a great degree of risk associated with online buying and therefore choose to buy it from a digital storefront rather than from an online marketplace to mitigate risk? Will a consumer require greater trust on the part of the online merchant when the perceived risk of online buying is rather high? We intend to find answers to these research questions through an empirical study. This paper explores the impact of enduring product involvement and perceived risks on required trust level, and further on online merchant choice. For the purpose of the research, five types or components of perceived risk are taken into consideration, including financial, performance, delivery, psychological, and social risks. A research model has been built around the constructs under consideration, and 12 hypotheses have been developed based on the research model to examine the relationships between enduring involvement and five components of perceived risk, between five components of perceived risk and required trust level, between enduring involvement and required trust level, and finally between required trust level and preference toward an e-tailer. To attain our research objectives, we conducted an empirical analysis consisting of two phases of data collection: a pilot test and main survey. The pilot test was conducted using 25 college students to ensure that the questionnaire items are clear and straightforward. Then the main survey was conducted using 295 college students at a major university for nine days between December 13, 2010 and December 21, 2010. The measures employed to test the model included eight constructs: (1) enduring involvement, (2) financial risk, (3) performance risk, (4) delivery risk, (5) psychological risk, (6) social risk, (7) required trust level, (8) preference toward an e-tailer. The statistical package, SPSS 17.0, was used to test the internal consistency among the items within the individual measures. Based on the Cronbach's ${\alpha}$ coefficients of the individual measure, the reliability of all the variables is supported. Meanwhile, the Amos 18.0 package was employed to perform a confirmatory factor analysis designed to assess the unidimensionality of the measures. The goodness of fit for the measurement model was satisfied. Unidimensionality was tested using convergent, discriminant, and nomological validity. The statistical evidences proved that the three types of validity were all satisfied. Now the structured equation modeling technique was used to analyze the individual paths along the relationships among the research constructs. The results indicated that enduring involvement has significant positive relationships with all the five components of perceived risk, while only performance risk is significantly related to trust level required by consumers for purchase. It can be inferred from the findings that product performance problems are mostly likely to occur when a merchant behaves in an opportunistic manner. Positive relationships were also found between involvement and required trust level and between required trust level and online merchant choice. Enduring involvement is concerned with the pleasure a consumer derives from a product class and/or with the desire for knowledge for the product class, and thus is likely to motivate the consumer to look for ways of mitigating perceived risk by requiring a higher level of trust on the part of the online merchant. Likewise, a consumer requiring a high level of trust on the merchant will choose a digital storefront rather than an e-marketplace, since a digital storefront is believed to be trustworthier than an e-marketplace, as it fulfills orders by itself rather than acting as an intermediary. The findings of the present research provide both academic and practical implications. The first academic implication is that enduring product involvement is a strong motivator of consumer responses, especially the selection of a merchant, in the context of electronic shopping. Secondly, academicians are advised to pay attention to the finding that an individual component or type of perceived risk can be used as an important research construct, since it would allow one to pinpoint the specific types of risk that are influenced by antecedents or that influence consequents. Meanwhile, our research provides implications useful for online merchants (both online storefronts and e-marketplaces). Merchants may develop strategies to attract consumers by managing perceived performance risk involved in purchase decisions, since it was found to have significant positive relationship with the level of trust required by a consumer on the part of the merchant. One way to manage performance risk would be to thoroughly examine the product before shipping to ensure that it has no deficiencies or flaws. Secondly, digital storefronts are advised to focus on symbolic goods (e.g., cars, cell phones, fashion outfits, and handbags) in which consumers are relatively more involved than others, whereas e- marketplaces should put their emphasis on non-symbolic goods (e.g., drinks, books, MP3 players, and bike accessories).

• Journal of Internet Computing and Services
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• v.16 no.1
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• pp.67-74
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• 2015

In recent years, frequent itemset mining for considering the importance of each item has been intensively studied as one of important issues in the data mining field. According to strategies utilizing the item importance, itemset mining approaches for discovering itemsets based on the item importance are classified as follows: weighted frequent itemset mining, frequent itemset mining using transactional weights, and utility itemset mining. In this paper, we perform empirical analysis with respect to frequent itemset mining algorithms based on transactional weights. The mining algorithms compute transactional weights by utilizing the weight for each item in large databases. In addition, these algorithms discover weighted frequent itemsets on the basis of the item frequency and weight of each transaction. Consequently, we can see the importance of a certain transaction through the database analysis because the weight for the transaction has higher value if it contains many items with high values. We not only analyze the advantages and disadvantages but also compare the performance of the most famous algorithms in the frequent itemset mining field based on the transactional weights. As a representative of the frequent itemset mining using transactional weights, WIS introduces the concept and strategies of transactional weights. In addition, there are various other state-of-the-art algorithms, WIT-FWIs, WIT-FWIs-MODIFY, and WIT-FWIs-DIFF, for extracting itemsets with the weight information. To efficiently conduct processes for mining weighted frequent itemsets, three algorithms use the special Lattice-like data structure, called WIT-tree. The algorithms do not need to an additional database scanning operation after the construction of WIT-tree is finished since each node of WIT-tree has item information such as item and transaction IDs. In particular, the traditional algorithms conduct a number of database scanning operations to mine weighted itemsets, whereas the algorithms based on WIT-tree solve the overhead problem that can occur in the mining processes by reading databases only one time. Additionally, the algorithms use the technique for generating each new itemset of length N+1 on the basis of two different itemsets of length N. To discover new weighted itemsets, WIT-FWIs performs the itemset combination processes by using the information of transactions that contain all the itemsets. WIT-FWIs-MODIFY has a unique feature decreasing operations for calculating the frequency of the new itemset. WIT-FWIs-DIFF utilizes a technique using the difference of two itemsets. To compare and analyze the performance of the algorithms in various environments, we use real datasets of two types (i.e., dense and sparse) in terms of the runtime and maximum memory usage. Moreover, a scalability test is conducted to evaluate the stability for each algorithm when the size of a database is changed. As a result, WIT-FWIs and WIT-FWIs-MODIFY show the best performance in the dense dataset, and in sparse dataset, WIT-FWI-DIFF has mining efficiency better than the other algorithms. Compared to the algorithms using WIT-tree, WIS based on the Apriori technique has the worst efficiency because it requires a large number of computations more than the others on average.

• Journal of Global Scholars of Marketing Science
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• v.18 no.1
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• pp.33-58
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• 2008

Previous research has presupposed that the evaluation of consumer who received any recovery after experiencing product failure should be better than the evaluation of consumer who did not receive any recovery. The major purposes of this article are to examine impacts of product defect failures rather than service failures, and to explore effects of recovery on postrecovery product attitudes. First, this article deals with the occurrence of severe and unsevere failure and corresponding service recovery toward tangible products rather than intangible services. Contrary to intangible services, purchase and usage are separable for tangible products. This difference makes it clear that executing an recovery strategy toward tangible products is not plausible right after consumers find out product failures. The consumers may think about backgrounds and causes for the unpleasant events during the time gap between product failure and recovery. The deliberation may dilutes positive effects of recovery efforts. The recovery strategies which are provided to consumers experiencing product failures can be classified into three types. A recovery strategy can be implemented to provide consumers with a new product replacing the old defective product, a complimentary product for free, a discount at the time of the failure incident, or a coupon that can be used on the next visit. This strategy is defined as "a rewarding effort." Meanwhile a product failure may arise in exchange for its benefit. Then the product provider can suggest a detail explanation that the defect is hard to escape since it relates highly to the specific advantage to the product. The strategy may be called as "a strengthening effort." Another possible strategy is to recover negative attitude toward own brand by giving prominence to the disadvantages of a competing brand rather than the advantages of its own brand. The strategy is reflected as "a weakening effort." This paper emphasizes that, in order to confirm its effectiveness, a recovery strategy should be compared to being nothing done in response to the product failure. So the three types of recovery efforts is discussed in comparison to the situation involving no recovery effort. The strengthening strategy is to claim high relatedness of the product failure with another advantage, and expects the two-sidedness to ease consumers' complaints. The weakening strategy is to emphasize non-aversiveness of product failure, even if consumers choose another competitive brand. The two strategies can be effective in restoring to the original state, by providing plausible motives to accept the condition of product failure or by informing consumers of non-responsibility in the failure case. However the two may be less effective strategies than the rewarding strategy, since it tries to take care of the rehabilitation needs of consumers. Especially, the relative effect between the strengthening effort and the weakening effort may differ in terms of the severity of the product failure. A consumer who realizes a highly severe failure is likely to attach importance to the property which caused the failure. This implies that the strengthening effort would be less effective under the condition of high product severity. Meanwhile, the failing property is not diagnostic information in the condition of low failure severity. Consumers would not pay attention to non-diagnostic information, and with which they are not likely to change their attitudes. This implies that the strengthening effort would be more effective under the condition of low product severity. A 2 (product failure severity: high or low) X 4 (recovery strategies: rewarding, strengthening, weakening, or doing nothing) between-subjects design was employed. The particular levels of product failure severity and the types of recovery strategies were determined after a series of expert interviews. The dependent variable was product attitude after the recovery effort was provided. Subjects were 284 consumers who had an experience of cosmetics. Subjects were first given a product failure scenario and were asked to rate the comprehensibility of the failure scenario, the probability of raising complaints against the failure, and the subjective severity of the failure. After a recovery scenario was presented, its comprehensibility and overall evaluation were measured. The subjects assigned to the condition of no recovery effort were exposed to a short news article on the cosmetic industry. Next, subjects answered filler questions: 42 items of the need for cognitive closure and 16 items of need-to-evaluate. In the succeeding page a subject's product attitude was measured on an five-item, six-point scale, and a subject's repurchase intention on an three-item, six-point scale. After demographic variables of age and sex were asked, ten items of the subject's objective knowledge was checked. The results showed that the subjects formed more favorable evaluations after receiving rewarding efforts than after receiving either strengthening or weakening efforts. This is consistent with Hoffman, Kelley, and Rotalsky (1995) in that a tangible service recovery could be more effective that intangible efforts. Strengthening and weakening efforts also were effective compared to no recovery effort. So we found that generally any recovery increased products attitudes. The results hint us that a recovery strategy such as strengthening or weakening efforts, although it does not contain a specific reward, may have an effect on consumers experiencing severe unsatisfaction and strong complaint. Meanwhile, strengthening and weakening efforts were not expected to increase product attitudes under the condition of low severity of product failure. We can conclude that only a physical recovery effort may be recognized favorably as a firm's willingness to recover its fault by consumers experiencing low involvements. Results of the present experiment are explained in terms of the attribution theory. This article has a limitation that it utilized fictitious scenarios. Future research deserves to test a realistic effect of recovery for actual consumers. Recovery involves a direct, firsthand experience of ex-users. Recovery does not apply to non-users. The experience of receiving recovery efforts can be relatively more salient and accessible for the ex-users than for non-users. A recovery effort might be more likely to improve product attitude for the ex-users than for non-users. Also the present experiment did not include consumers who did not have an experience of the products and who did not perceive the occurrence of product failure. For the non-users and the ignorant consumers, the recovery efforts might lead to decreased product attitude and purchase intention. This is because the recovery trials may give an opportunity for them to notice the product failure.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.185-202
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• 2012

.Since the value of information has been realized in the information society, the usage and collection of information has become important. A facial expression that contains thousands of information as an artistic painting can be described in thousands of words. Followed by the idea, there has recently been a number of attempts to provide customers and companies with an intelligent service, which enables the perception of human emotions through one's facial expressions. For example, MIT Media Lab, the leading organization in this research area, has developed the human emotion prediction model, and has applied their studies to the commercial business. In the academic area, a number of the conventional methods such as Multiple Regression Analysis (MRA) or Artificial Neural Networks (ANN) have been applied to predict human emotion in prior studies. However, MRA is generally criticized because of its low prediction accuracy. This is inevitable since MRA can only explain the linear relationship between the dependent variables and the independent variable. To mitigate the limitations of MRA, some studies like Jung and Kim (2012) have used ANN as the alternative, and they reported that ANN generated more accurate prediction than the statistical methods like MRA. However, it has also been criticized due to over fitting and the difficulty of the network design (e.g. setting the number of the layers and the number of the nodes in the hidden layers). Under this background, we propose a novel model using Support Vector Regression (SVR) in order to increase the prediction accuracy. SVR is an extensive version of Support Vector Machine (SVM) designated to solve the regression problems. The model produced by SVR only depends on a subset of the training data, because the cost function for building the model ignores any training data that is close (within a threshold ${\varepsilon}$) to the model prediction. Using SVR, we tried to build a model that can measure the level of arousal and valence from the facial features. To validate the usefulness of the proposed model, we collected the data of facial reactions when providing appropriate visual stimulating contents, and extracted the features from the data. Next, the steps of the preprocessing were taken to choose statistically significant variables. In total, 297 cases were used for the experiment. As the comparative models, we also applied MRA and ANN to the same data set. For SVR, we adopted '${\varepsilon}$-insensitive loss function', and 'grid search' technique to find the optimal values of the parameters like C, d, ${\sigma}^2$, and ${\varepsilon}$. In the case of ANN, we adopted a standard three-layer backpropagation network, which has a single hidden layer. The learning rate and momentum rate of ANN were set to 10%, and we used sigmoid function as the transfer function of hidden and output nodes. We performed the experiments repeatedly by varying the number of nodes in the hidden layer to n/2, n, 3n/2, and 2n, where n is the number of the input variables. The stopping condition for ANN was set to 50,000 learning events. And, we used MAE (Mean Absolute Error) as the measure for performance comparison. From the experiment, we found that SVR achieved the highest prediction accuracy for the hold-out data set compared to MRA and ANN. Regardless of the target variables (the level of arousal, or the level of positive / negative valence), SVR showed the best performance for the hold-out data set. ANN also outperformed MRA, however, it showed the considerably lower prediction accuracy than SVR for both target variables. The findings of our research are expected to be useful to the researchers or practitioners who are willing to build the models for recognizing human emotions.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1041-1043
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• 1993

Fuzzy logic based Control Theory has gained much interest in the industrial world, thanks to its ability to formalize and solve in a very natural way many problems that are very difficult to quantify at an analytical level. This paper shows a solution for treating membership function inside hardware circuits. The proposed hardware structure optimizes the memoried size by using particular form of the vectorial representation. The process of memorizing fuzzy sets, i.e. their membership function, has always been one of the more problematic issues for the hardware implementation, due to the quite large memory space that is needed. To simplify such an implementation, it is commonly [1,2,8,9,10,11] used to limit the membership functions either to those having triangular or trapezoidal shape, or pre-definite shape. These kinds of functions are able to cover a large spectrum of applications with a limited usage of memory, since they can be memorized by specifying very few parameters ( ight, base, critical points, etc.). This however results in a loss of computational power due to computation on the medium points. A solution to this problem is obtained by discretizing the universe of discourse U, i.e. by fixing a finite number of points and memorizing the value of the membership functions on such points [3,10,14,15]. Such a solution provides a satisfying computational speed, a very high precision of definitions and gives the users the opportunity to choose membership functions of any shape. However, a significant memory waste can as well be registered. It is indeed possible that for each of the given fuzzy sets many elements of the universe of discourse have a membership value equal to zero. It has also been noticed that almost in all cases common points among fuzzy sets, i.e. points with non null membership values are very few. More specifically, in many applications, for each element u of U, there exists at most three fuzzy sets for which the membership value is ot null [3,5,6,7,12,13]. Our proposal is based on such hypotheses. Moreover, we use a technique that even though it does not restrict the shapes of membership functions, it reduces strongly the computational time for the membership values and optimizes the function memorization. In figure 1 it is represented a term set whose characteristics are common for fuzzy controllers and to which we will refer in the following. The above term set has a universe of discourse with 128 elements (so to have a good resolution), 8 fuzzy sets that describe the term set, 32 levels of discretization for the membership values. Clearly, the number of bits necessary for the given specifications are 5 for 32 truth levels, 3 for 8 membership functions and 7 for 128 levels of resolution. The memory depth is given by the dimension of the universe of the discourse (128 in our case) and it will be represented by the memory rows. The length of a world of memory is defined by: Length = nem (dm(m)＋dm(fm) Where: fm is the maximum number of non null values in every element of the universe of the discourse, dm(m) is the dimension of the values of the membership function m, dm(fm) is the dimension of the word to represent the index of the highest membership function. In our case then Length=24. The memory dimension is therefore 128*24 bits. If we had chosen to memorize all values of the membership functions we would have needed to memorize on each memory row the membership value of each element. Fuzzy sets word dimension is 8*5 bits. Therefore, the dimension of the memory would have been 128*40 bits. Coherently with our hypothesis, in fig. 1 each element of universe of the discourse has a non null membership value on at most three fuzzy sets. Focusing on the elements 32,64,96 of the universe of discourse, they will be memorized as follows: The computation of the rule weights is done by comparing those bits that represent the index of the membership function, with the word of the program memor . The output bus of the Program Memory (μCOD), is given as input a comparator (Combinatory Net). If the index is equal to the bus value then one of the non null weight derives from the rule and it is produced as output, otherwise the output is zero (fig. 2). It is clear, that the memory dimension of the antecedent is in this way reduced since only non null values are memorized. Moreover, the time performance of the system is equivalent to the performance of a system using vectorial memorization of all weights. The dimensioning of the word is influenced by some parameters of the input variable. The most important parameter is the maximum number membership functions (nfm) having a non null value in each element of the universe of discourse. From our study in the field of fuzzy system, we see that typically nfm 3 and there are at most 16 membership function. At any rate, such a value can be increased up to the physical dimensional limit of the antecedent memory. A less important role n the optimization process of the word dimension is played by the number of membership functions defined for each linguistic term. The table below shows the request word dimension as a function of such parameters and compares our proposed method with the method of vectorial memorization[10]. Summing up, the characteristics of our method are: Users are not restricted to membership functions with specific shapes. The number of the fuzzy sets and the resolution of the vertical axis have a very small influence in increasing memory space. Weight computations are done by combinatorial network and therefore the time performance of the system is equivalent to the one of the vectorial method. The number of non null membership values on any element of the universe of discourse is limited. Such a constraint is usually non very restrictive since many controllers obtain a good precision with only three non null weights. The method here briefly described has been adopted by our group in the design of an optimized version of the coprocessor described in [10].

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.93-111
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• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.

• Journal of the Korean Society of School Health
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• v.9 no.1
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• pp.109-123
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• 1996

The study was designed to gain necessary basic data, in order to grasp the actual condition on personal hygiene of primary school students and to help school health education and child health education data development of parents of students, the survery was carried out through this reporter's interview for mothers who have primary school pupil in KyungBug area during the period of a month from 14 the April to 30 the April 1994. The results of this study can be summarized as follows. 1. As for general characteristics, the percent of answer mothers with 30~34 years 44.9%, and the percent of answer mothers with 35~39 years was 37.6%, 14.6% of answer mother was 40 years and over. The most house types was apartment house, that is 64.8% and independence house was 18.9%, 13.7% of answer mothers was multivalent house residents. As for level of school career, the percent of high school was 64.2% and the percent of university was 20.5%, 10.1% of answer mothers was finishment of middle school. As for religion a lack of religion was 38.4% and a Buddhist was 36.8%, a christian was 13.4%, 10.4% of answer mothers was a catholic. As for jobs, the percent of office was 33.9%, specialist and expert skiller was 17.3%, self-management was 16.0%, fishing and agrarian villiages was 11.6%, public service personnel and shool personnel was 9.1%. As for the number of sons and daughters, the percent of two was 70.5%, the percent of one was 16.0%, and the percent of male students was 54.1%. As for level of economic life middle level was 59.9%. 2. The percentage of washing hand after school hours was 66.8% and not washing hand after school hours was 33.2%. In washing hand after school hours family of independence house, mothers that have schoolcareer of university, female students, three and over of sons and daughters was high individually (p<0.05). 3. As for paring one's nails, once a ten days was 52.9% once a five days was 22.5% once a fifteen days was 19.0%, once a twenty and over was 5.6%. 4. 54.7% of primary school students of answer mother's take a bath once a few days and 31.6% take a bath once a week, 10.1% take a bath once a tendays. 5. The percentage of changing of underwear once a day was 60.9%, once a few days was 37.1%, once a week was 2.0%, as for changing of underwear, sons and daughters that has mothers with 29 years and downward, one number of sons and daughters, females students was high individually (p<0.01). 6. The percentage of haircut once a 20~30 days was 59.9%, once a 31~40 days was 17.9%, once a 40 days and over was 16.6%. As for the percentage of haircut once a 20~30 days apartment house residents, male students was high (p<0.01). 7. The percentage of experience in taking nutrition was 79.8% and the percentage of experience in taking restorative was 72.3%. As for taking nutrition, apartment house residents, one number of sons and daughters, male students was high individually (p<0.01). As for taking restorative apartment house residents, on number of sons and daughters, male students was high individually (p<0.05). 8. The percentage of habit of unbalanced diet was 44.0%, sons and daughters that have mother with 40 years and over apartment house residents, male students, one number of sons and daughters was high indivdually (p<0.05). 9. As for hygiene condition of sons and daughters, the percentage of good state of health was 65.2%, middle state of health was 3.5% bad state of health was 11.4%. In good state of health sons and daughters that have mothers with 29 years and downward, multivalent house residents, three numbers of sons and daughters, female students, high birth was high individually. 10. As for fattness of sons and daughters, existence was 18.2%. No existence was 81.8%. in existence sons and daughters who have mothers with 40 years and downward, independence house resident, special job and expert skill job, three and over number of sons and daughters, female students, low birth was high individually. 11. As for use aspect of medical facilities of sons and daughters, hospital doctors was 53.1%, drugstore was 42.3%, chinese medicine hospitals or health organization was 4.6%. In usage of drugstore, sons and daughters of mothers with 29 years, 40 years and over was 55.6%, 61.4% individully, inusage of hospital doctors 30~34 years, 34~39 tears was 64.5%, 49.1% individully (p<0.01), apartment house residents, one or two numbers of sons and daughters, male students was high individually. In the percentage of using drugstore, school career of middle school and downward, in occupation, three and over numbers of sons and daughters, low birth was high individually (p<0.05). According to the results mentioned above. An actual condition and a related matters on personal hygiene of primary school students must be used as the basis data of a health education program and a health data of a health education program and a health data related, a teaching materials development and must be helped to the health life education of parents of students and childs.

• Korean Journal of Heritage: History & Science
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• v.48 no.3
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• pp.96-119
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• 2015

This research has studied the changes of Gwi-po(轉角包) by taking the cases of China's medieval wooden buildings as objects. The purpose of the study is to examine the time-periodic transition process of Gwi-po through the cases of 71 wooden buildings which were built from Tang(唐) dynasty(AD 618~690 & 705~907) until Jin(金) dynasty(AD 1115~1234) and also designated as 'Major Historical and Cultural Sites Protected at the National Level'. This research has taken note of various frame types of Jwau-dae(左右隊), which are architectural components of Gwi-po, to study the changes and development process of Gwi-po. The results are as follows. An important factor in the transformations of Gwi-po format is the changes in perception of the craftsmen about Jwau-dae, who took charge in the building process. In the early periods, the principles of Yidou sanshen dougong(一斗三升) in constructing ancons of Gwi-po had been well-maintained, while there appeared many different types of Gwi-po in later periods, due to the usage of Jwau-dae and $Shu{\check{a}}$ $t{\acute{o}}u$(?頭) in each Chulmok of Gwi-po. Transitional types of Gwi-po, which were evolved from the earlier ones, are divided into 3 categories by different forms of Jwau-dae, placed on odd number stages. The first one is 'none-$f{\bar{a}}ng$ $t{\acute{o}}u$(無枋頭) type' of Song(AD 960~1127, 1127~1279) and Liao dynasty(AD 907~1125) buildings, which doesn't have $f{\bar{a}}ng$ $t{\acute{o}}u$(枋頭)s, for the reason that Jwau-dae(左右隊) is in direct contact with Gwihan-dae(耳限大). The second one is '$Shu{\check{a}}$ $t{\acute{o}}u$ $f{\bar{a}}ng$ $t{\acute{o}}u$(?頭枋頭) type' of Song(AD 960~1127, 1127~1279) and Jin dynasty(AD 1115~1234), that has $f{\bar{a}}ng$ $t{\acute{o}}u$(枋頭)s of Jwau-dae(左右隊) identical to $Shu{\check{a}}$ $t{\acute{o}}u$(?頭) in form. The last one is '$Xi{\check{a}}o$ $g{\check{o}}ng$ $t{\acute{o}}u$(小?頭) type' of Jin(AD 1115~1234) and Yuan dynasty(AD 1271~1368), which has $f{\bar{a}}ng$ $t{\acute{o}}u$(枋頭)s of Jwau-dae identical to $Xi{\check{a}}o$ $g{\check{o}}ng$ $t{\acute{o}}u$(小?頭) in form. The earlier forms of Gwi-po, which appeared between Tang dynasty(AD 618~690 & 705~907) and Five Dynasties periods(907~960) went through transitional forms of 'non-$f{\bar{a}}ng$ $t{\acute{o}}u$(無枋頭) type', '$Shu{\check{a}}$ $t{\acute{o}}u$ $f{\bar{a}}ng$ $t{\acute{o}}u$(?頭枋頭) type' and '$Xi{\check{a}}o$ $g{\check{o}}ng$ $t{\acute{o}}u$(小?頭) type' and finally had its form settled between Yuan(元, AD 1271~1368) and Ming(明. AD 1368~1644) dynasty periods. In Liao(遼) dynasty period(AD 907~1125), as the buildings got bigger and the tendency of longer eave-exposure was implemented, there grew a certain need to structurally reinforce Gwi-po, on which load of the whole roof is concentrated. Especially, the transition from Tōuxīn $z{\grave{a}}o$(偸心造) style to Jì xīn $z{\grave{a}}o$(計心造) style in this period had a great influence on standardization of Gwi-po, along with None-${\acute{A}}ng$(無仰) style. Furthermore, Wing-type Gong(翼型?), which developed in Liao dynasty(AD 907~1125), is also thought to have had a great influence on the transition from Tōuxīn $z{\grave{a}}o$(偸心造) style to Jì xīn $z{\grave{a}}o$(計心造) style by changing the forms of Gongs(?), such as Gwi-po. However, unlike None-${\acute{A}}ng$(無仰) style, there occurred a gradual change from '$Shu{\check{a}}$ $t{\acute{o}}u$ $f{\bar{a}}ng$ $t{\acute{o}}u$(?頭枋頭) type' to '$Xi{\check{a}}o$ $g{\check{o}}ng$ $t{\acute{o}}u$(小?頭) type' of Gwi-po in $Xi{\grave{a}}$ ${\acute{a}}ng$ style.

• Korean Journal of Environmental Agriculture
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• v.38 no.3
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• pp.173-184
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• 2019

BACKGROUND: This study was carried out to investigate pesticide residues from fifty streams in Korea. Water samples were collected at two times. Thee first sampling was performed from april to may, which was the season for start of pesticide application and the second sampling event was from august to september, which was a period for spraying pesticides multiple times. METHODS AND RESULTS: The 136 pesticide residues were analyzed by LC-MS/MS and GC/ECD. As a result, eleven of the pesticide residues were detected at the first sampling. Twenty eight of the pesticide residues were detected at the second sampling. Seven pesticides were frequently detected from more than 10 water samples. Ecological risk assessment (ERA) was carried out by using residual and toxicological data. Four scenarios were applied for the ERA. Scenario 1 and 2 were performed using LC50 values and mean and maximum concentrations. Scenarios 3 and 4 were conducted by NOEC values and mean and maximum concentrations. CONCLUSION: Frequently detected pesticide residues tended to coincide with the period of preventing pathogen and pest at paddy rice. As a result of ERA, five pesticides (butachlor, carbendazim, carbofuran, chlorantranilprole, and oxadiazon) were assessed to be risks at scenario 4. However, only oxadiazon was assessed to be a risk at scenario 3 for the first sampling. Oxadiazon was not assessed to be a risk at the second sampling. It seems to be temporary phenomenon at the first sampling, because usage of herbicides such as oxadiazon increased from April to march for preventing weeds at paddy fields. However, this study suggested that five pesticides which were assessed to be risks need to be monitored continuously for the residues.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.