• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.299-307
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• 2014

Nowadays, SNS media has become one of the most common and economic communication methods in the modern word. With the rapid development of communication technology, a variety of SNS media makes easier for us to reach other people everywhere and people are getting more and more reliant on them. The surge in SNS media users and SNS media diversity gets more important every day, but academic research on SNS media evaluation program is scanty. In order to present an evaluation program on the SNS media, this study selected some applicable measurement factors based on reference research, and then evaluate major SNS medias by using AHP analysis method. With these purposes, Kakaotalk, Twitter and Facebook, are the three major SNS medias that were chosen for the analysis. Through collections and analysis of usability evaluation factors about SNS quality from related literature, some factors were concluded: system quality, information quality, interface quality and service quality, to build an AHP frame. And to achieve these purposes, this study applied comprehend mutual questionnaire survey for experts and users to gather research date. Empirical studies about the results were performed using AHP method with Microsoft Office Excel 2010. In this study, the final SNS media evaluation result is that Kakaotalk ranks first of the three SNS media, Facebook is in the second place, and Twitter as least. When SNS media are evaluated, system quality, information quality, interface quality and service quality should be considered. Among all factors, service quality is the most important.

• Journal of Distribution Science
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• v.14 no.7
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• pp.83-90
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• 2016

Purpose - This study focuses on new type distribution channel that named as Omnichannel. Omnichannel is developed from Multichannel which is used in many distribution channels to buy or selling goods. Omnichannel basically needs an Information and Communications Technologies(ICT) to use, so researcher conduct a Technology Acceptance Model(TAM) to research model. Customer-brand relationship was used as dependent variable to focus on the role of Omnichannel. Research design, data, and methodology - The subject of this study is customer who purchase goods or service through omnichannel. Based on the literature from the preceding research analysis of TAM and customer-brand relationship, this study was constructed by the reference to previous studies, final research model design for figure out casual relationship among perceived ease of use, perceived usefulness, omnichannel use and customer-brand relationship. From 2016 February 3 to March 17, questionnaire survey targeted customers who use online and offline channels. 273 questionnaire survey had conducted, then, 252 survey data were available for empirical analysis. Researcher provide descriptive statistics for checking generality. Cronbach's alpha value was used to check the reliability of data. Exploratory factor analysis was used for purification of values and eigenvalue checking. After EFA, Confirmatory factor analysis was used to prepare structural equation modeling with executing structural equation modeling for confirming hypothesis which developed by researcher. Results - The main results of this empirical study are as follows. First, omnichannel's perceived ease of use has positive significant effect on perceived usefulness(estimate: 0.579). Moreover, omnichannel's perceived ease of use and perceived usefulness has positive significant effect on omnichannel use(estimate: 0.325,0.648). Second, using omnichannel has positive significant effect on brand-customer relationship(estimate: 0.521). Every hypothesis adopted as researcher designed. This study found out the intermediate relationship between perceived ease of use and omnichannel use by investigating hypothesis. Conclusions - Base on the empirical result, this study confirmed that TAM theory perceived has relation with omnichannel. First, factors of TAM has positive effect on omnichannel use, so it highlights the important role of customer based interface and usefulness. Especially, perceived usefulness has high indirect influence on ease of use and use of omnichannel. It seems that when customers try to decide use or not use omnichannel, customers focus on percept benefits from omnichannel. Thus, a provider should applicate attractive price table, accurate product or service information and high switching cost strategy to emphasize the usefulness of omnichannel. Second, using omnichannel enhances the relationship between customers and brand, because there are more time and frequency to serve customers. It is important because good relationship between customers can increase the future's financial performance through word of mouse, positive brand image and loyalty to brand or company. Finally, despite of empirical result and implications, this study has limitations. First, there are only a few previous studies about omnicahnnel, so literature reviews are restricted. While set up the factors which can affect the use of omnichannel, next study should be considered with broader theories or models(ex: contingency theory). Second, omnichannel has developed from multichannel, so comparative analysis is needed between these methods because there is a possibility about different forte character of each distribution system on customer's consuming patterns.

• Journal of Educational Research in Mathematics
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• v.22 no.1
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• pp.53-68
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• 2012

This paper investigated the conceptual schemes four children constructed as they related division number sentences to various types of fraction: Proper fractions, improper fractions, and mixed numbers in both contextual and abstract symbolic forms. Methods followed those of the constructivist teaching experiment. Four fifth-grade students from an inner city school in the southwest United States were interviewed eight times: Pre-test clinical interview, six teaching / semi-structured interviews, and a final post-test clinical interview. Results showed that for equal sharing situations, children conceptualized division in two ways: For mixed numbers, division generated a whole number portion of quotient and a fractional portion of quotient. This provided the conceptual basis to see improper fractions as quotients. For proper fractions, they tended to see the quotient as an instance of the multiplicative structure: $a{\times}b=c$ ; $a{\div}c=\frac{1}{b}$ ; $b{\div}c=\frac{1}{a}$. Results suggest that first, facility in recall of multiplication and division fact families and understanding the multiplicative structure must be emphasized before learning fraction division. Second, to facilitate understanding of the multiplicative structure children must be fluent in representing division in the form of number sentences for equal sharing word problems. If not, their reliance on long division hampers their use of syntax and their understanding of divisor and dividend and their relation to the concepts of numerator and denominator.

• Journal of KIISE:Software and Applications
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• v.29 no.4
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• pp.270-277
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• 2002

Text filtering is a task of deciding whether a document has relevance to a specified topic. As Internet and Web becomes wide-spread and the number of documents delivered by e-mail explosively grows the importance of text filtering increases as well. The aim of this paper is to improve the accuracy of text filtering systems by using machine learning techniques. We apply AdaBoost algorithms to the filtering task. An AdaBoost algorithm generates and combines a series of simple hypotheses. Each of the hypotheses decides the relevance of a document to a topic on the basis of whether or not the document includes a certain word. We begin with an existing AdaBoost algorithm which uses weak hypotheses with their output of 1 or -1. Then we extend the algorithm to use weak hypotheses with real-valued outputs which was proposed recently to improve error reduction rates and final filtering performance. Next, we attempt to achieve further improvement in the AdaBoost's performance by first setting weights randomly according to the continuous Poisson distribution, executing AdaBoost, repeating these steps several times, and then combining all the hypotheses learned. This has the effect of mitigating the ovefitting problem which may occur when learning from a small number of data. Experiments have been performed on the real document collections used in TREC-8, a well-established text retrieval contest. This dataset includes Financial Times articles from 1992 to 1994. The experimental results show that AdaBoost with real-valued hypotheses outperforms AdaBoost with binary-valued hypotheses, and that AdaBoost iterated with random weights further improves filtering accuracy. Comparison results of all the participants of the TREC-8 filtering task are also provided.

• Journal of the Korean Society of Earth Science Education
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• v.16 no.1
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• pp.153-165
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• 2023

This study investigates learners' environmental literacy, classifies the results by factors of environmental literacy, and then investigates the differences in the students' environmental behavior practice experiences according to the classification by factor. The study was conducted with 47 6th grade students from D elementary school located in P metropolitan city as the subject of final analysis, and environmental literacy questionnaires and environmental behavior practice experience questionnaires were used as the main data. As a result of the study, the learners were classified into three groups according to the factors of environmental literacy, and they were respectively named as the "High environmental literacy group", "low environmental literacy group", and "Low Function and Affectif group". A Word network was formed using the descriptions of environmental behavior practice experiences for each cluster, and a Degree Centrality Analysis was performed to visualize and then analyze. As a result of the analysis, "High environmental literacy group" was confirmed, 1) recognized the subjects of environmental action practice as individuals and families, 2) described his experience of environmental action practice in relation to all elements of environmental literacy, and had a relatively pessimistic view. "low environmental literacy group", and "Low Function and Affectif group" were confirmed 1) perceive the subject of environmental behavior practice as a relatively social problem, 2) the description of the experience of environmental behavior practice is relatively biased specific factors, and the "Low Function and Affectif group" is particularly focused on the knowledge element. And 3) it was confirmed that they were aware of climate change from a relatively optimistic perspective. Based on this conclusion, suggestions were made from the perspective of environmental education.

• Sim-seong Yeon-gu
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• v.30 no.1
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• pp.1-30
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• 2015

The Red Book contains C.G. Jung's insightful comment on life suggesting the thoughts of the Eastern philosophers, particularly that of Lao Zi. The author reviewed Jung's commentaries in the Red Book in comparison with Lao Zi Dao De Jing. Jung's comments on the image of despised Surpreme Being, on the Simplicity, the attitudes of 'the Spirit of the Depth' toward intellectual knowledges and speech, toward the small and the mockered one resemble to what Lao Zi spoke on Dao in his Dao De Jing. The 'good and evil' are regarded by both C.G. Jung and Lao Zi as two poles in one total psyche. The favorite words of Lao Zi : 'emptiness' or 'empty' are frequently mentioned in the Red Book. The investigation in this concern revealed that C.G. Jung, contrary to Lao Zi has applied the word 'emptiness' mostly as the opposite to the fullness. C.G. Jung's way of encountering with the darkest side of soul in the Hell and his bold confrontation to the authoritative person such as Philemon, above all, the intensity of his experiences in the state of the utmost tension between the opposites are extraordinarily impressive and somehow strange when regarded from traditional eastern way of behavior such as I-You relationship and the patterns of emotional life based on Confucian tradition. Confucius never talked about the prodigies, feasts of strength and disorders or spirits. Lao Zi never mentioned infernal cruelty. Noteworthy is however, both have enough experienced the cruelty of life and conflicts in the reality and what they spoke was not a process in search for solution but the final proposals for the solution of human agony. C.G. Jung was, like great shaman in central and East-Asia forced to go through inferno in his unique way and from these experiences obtained the insight which resembles not only to Lao Zi but also to wisdoms from the western philosophies and also from the Christianity.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.1-25
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• 2020

Sentiment Analysis (SA) is a Natural Language Processing (NLP) task that analyzes the sentiments consumers or the public feel about an arbitrary object from written texts. Furthermore, Aspect-Based Sentiment Analysis (ABSA) is a fine-grained analysis of the sentiments towards each aspect of an object. Since having a more practical value in terms of business, ABSA is drawing attention from both academic and industrial organizations. When there is a review that says "The restaurant is expensive but the food is really fantastic", for example, the general SA evaluates the overall sentiment towards the 'restaurant' as 'positive', while ABSA identifies the restaurant's aspect 'price' as 'negative' and 'food' aspect as 'positive'. Thus, ABSA enables a more specific and effective marketing strategy. In order to perform ABSA, it is necessary to identify what are the aspect terms or aspect categories included in the text, and judge the sentiments towards them. Accordingly, there exist four main areas in ABSA; aspect term extraction, aspect category detection, Aspect Term Sentiment Classification (ATSC), and Aspect Category Sentiment Classification (ACSC). It is usually conducted by extracting aspect terms and then performing ATSC to analyze sentiments for the given aspect terms, or by extracting aspect categories and then performing ACSC to analyze sentiments for the given aspect category. Here, an aspect category is expressed in one or more aspect terms, or indirectly inferred by other words. In the preceding example sentence, 'price' and 'food' are both aspect categories, and the aspect category 'food' is expressed by the aspect term 'food' included in the review. If the review sentence includes 'pasta', 'steak', or 'grilled chicken special', these can all be aspect terms for the aspect category 'food'. As such, an aspect category referred to by one or more specific aspect terms is called an explicit aspect. On the other hand, the aspect category like 'price', which does not have any specific aspect terms but can be indirectly guessed with an emotional word 'expensive,' is called an implicit aspect. So far, the 'aspect category' has been used to avoid confusion about 'aspect term'. From now on, we will consider 'aspect category' and 'aspect' as the same concept and use the word 'aspect' more for convenience. And one thing to note is that ATSC analyzes the sentiment towards given aspect terms, so it deals only with explicit aspects, and ACSC treats not only explicit aspects but also implicit aspects. This study seeks to find answers to the following issues ignored in the previous studies when applying the BERT pre-trained language model to ACSC and derives superior ACSC models. First, is it more effective to reflect the output vector of tokens for aspect categories than to use only the final output vector of [CLS] token as a classification vector? Second, is there any performance difference between QA (Question Answering) and NLI (Natural Language Inference) types in the sentence-pair configuration of input data? Third, is there any performance difference according to the order of sentence including aspect category in the QA or NLI type sentence-pair configuration of input data? To achieve these research objectives, we implemented 12 ACSC models and conducted experiments on 4 English benchmark datasets. As a result, ACSC models that provide performance beyond the existing studies without expanding the training dataset were derived. In addition, it was found that it is more effective to reflect the output vector of the aspect category token than to use only the output vector for the [CLS] token as a classification vector. It was also found that QA type input generally provides better performance than NLI, and the order of the sentence with the aspect category in QA type is irrelevant with performance. There may be some differences depending on the characteristics of the dataset, but when using NLI type sentence-pair input, placing the sentence containing the aspect category second seems to provide better performance. The new methodology for designing the ACSC model used in this study could be similarly applied to other studies such as ATSC.

• Asia Marketing Journal
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• v.12 no.2
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• pp.111-133
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• 2010

Marketing financial services used to be easier. Today, the competition in financial services is fierce. Not only has the competition become more intense, financial services have also changed structurally. In an environment with various customer needs and severe competitions, the marketing in financial services industry is getting more difficult and more important than before. However, there are still not enough studies on financial services marketing in Korea whereas lots of research papers have been published frequently in some international journals. The purpose of this paper is (1)to review the literature on financial services marketing, (2)to investigate current marketing activities based on in-depth interview with financial marketing managers in Korea, and (3)to suggest some implications for future research on the financial services marketing. Financial products are not consumer products. In fact, they are not products at all in the way product marketing is usually described. Nor are they altogether like services. The financial industry operates in a unique way, and its marketing tasks are correspondingly complex. However, the literature review shows that there has been a lack of basic studies which dealt with inherent characteristics of financial services marketing compared to the research on marketing in other industries. Many studies in domestic marketing journals have so far focused only on the general customer behaviors and the special issues in some financial industries. However, for more effective financial services marketing, we have to answer following questions. Is there any difference between financial service marketing and consumer packaged goods marketing? What are the differences between the financial services marketing and other services marketing such as education and health services? Are there different ways of marketing among banks, securities firms, insurance firms, and credit card companies? In other words, we need more detailed research as well as basic studies about the financial services marketing. For example, we need concrete definitions of financial services marketing, bank marketing, securities firm marketing, and etc. It is also required to compare the characteristics of each marketing within the financial services industry. The products sold in each market have different characteristics such as duration and degree of risk-taking. It means that there are sub-categories in financial services marketing. We have to consider them in the future research on the financial services marketing. It is also necessary to study customer decision making process in the financial markets. There have been little research on how customers search and process information, compare alternatives, make final decision, and repeat their choices. Because financial services have some unique characteristics, we need different understandings in the customer behaviors compared to the behaviors in other service markets. And also considering the rapid growth in financial markets and upcoming severe competition between domestic and global financial companies, it is time to start more systematic and detailed research on financial services marketing in Korea. In the second part of this paper, I analyzed the results of in-depth interview with 20 marketing managers of financial services companies in Korea. As a result, I found that the role of marketing departments in Korean financial companies are mainly focused on the short-term activities such as sales support, promotion, and CRM data analysis although the size and history of marketing departments to some extent show a sign of maturity. Most companies established official marketing departments before 2001. Average number of employees in a marketing department is about 58. However, marketing managers in eight companies(40% of the sample) still think that the purpose of marketing is only to support and manage general sales activities. It shows that some companies have sales-oriented concept rather than marketing-oriented concept. I also found three key words which marketing managers think importantly in financial services markets. They are (1)Trust in customer relationship, (2)Brand differentiation, and (3)Rapid response to customer needs. 50% of the sample support that "Trust" is the most important key word in the financial services marketing. It is interesting that 80% of banks and securities companies think that "Trust" is the most important thing, whereas managers in credit card companies consider "Rapid response to customer needs" as the most important key word in their market. In addition, there are different problems recognition of marketing managers depending on the types of financial industries they belong to. For example, in the case of banks and insurance companies, marketing managers consider "a lack of communication with other departments" as the most serious problem. On the other hand, in the case of securities firms, "a lack of utilization of customer data" is the most serious problem. These results imply that there are different important factors for the customer satisfaction depending on the types of financial industries, and managers have to consider them when marketing financial products in more effective ways. For example, It will be necessary for marketing managers to study different important factors which affect customer satisfaction, repeat purchase, degree of risk-taking, and possibility of cross-selling according to the types of financial industries. I also suggested six hypothetical propositions for the future research.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.63-82
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• 2016

Individuals gather information online to resolve problems in their daily lives and make various decisions about the purchase of products or services. With the revolutionary development of information technology, Web 2.0 has allowed more people to easily generate and use online reviews such that the volume of information is rapidly increasing, and the usefulness and significance of analyzing the unstructured data have also increased. This paper presents an analysis on the lexical features of expert product reviews to determine their influence on consumers' purchasing decisions. The focus was on how unstructured data can be organized and used in diverse contexts through text mining. In addition, diverse lexical features of expert reviews of contents provided by a third-party review site were extracted and defined. Expert reviews are defined as evaluations by people who have expert knowledge about specific products or services in newspapers or magazines; this type of review is also called a critic review. Consumers who purchased products before the widespread use of the Internet were able to access expert reviews through newspapers or magazines; thus, they were not able to access many of them. Recently, however, major media also now provide online services so that people can more easily and affordably access expert reviews compared to the past. The reason why diverse reviews from experts in several fields are important is that there is an information asymmetry where some information is not shared among consumers and sellers. The information asymmetry can be resolved with information provided by third parties with expertise to consumers. Then, consumers can read expert reviews and make purchasing decisions by considering the abundant information on products or services. Therefore, expert reviews play an important role in consumers' purchasing decisions and the performance of companies across diverse industries. If the influence of qualitative data such as reviews or assessment after the purchase of products can be separately identified from the quantitative data resources, such as the actual quality of products or price, it is possible to identify which aspects of product reviews hamper or promote product sales. Previous studies have focused on the characteristics of the experts themselves, such as the expertise and credibility of sources regarding expert reviews; however, these studies did not suggest the influence of the linguistic features of experts' product reviews on consumers' overall evaluation. However, this study focused on experts' recommendations and evaluations to reveal the lexical features of expert reviews and whether such features influence consumers' overall evaluations and purchasing decisions. Real expert product reviews were analyzed based on the suggested methodology, and five lexical features of expert reviews were ultimately determined. Specifically, the "review depth" (i.e., degree of detail of the expert's product analysis), and "lack of assurance" (i.e., degree of confidence that the expert has in the evaluation) have statistically significant effects on consumers' product evaluations. In contrast, the "positive polarity" (i.e., the degree of positivity of an expert's evaluations) has an insignificant effect, while the "negative polarity" (i.e., the degree of negativity of an expert's evaluations) has a significant negative effect on consumers' product evaluations. Finally, the "social orientation" (i.e., the degree of how many social expressions experts include in their reviews) does not have a significant effect on consumers' product evaluations. In summary, the lexical properties of the product reviews were defined according to each relevant factor. Then, the influence of each linguistic factor of expert reviews on the consumers' final evaluations was tested. In addition, a test was performed on whether each linguistic factor influencing consumers' product evaluations differs depending on the lexical features. The results of these analyses should provide guidelines on how individuals process massive volumes of unstructured data depending on lexical features in various contexts and how companies can use this mechanism from their perspective. This paper provides several theoretical and practical contributions, such as the proposal of a new methodology and its application to real data.