• Cartoon and Animation Studies
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• s.18
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• pp.57-72
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• 2010

Textbooks used in Information Society need to use many illustrations and pictures which have positive effect in motivating and triggering students to study. "Pictures mean more than thousands of the word" says that it is significant to use media in class through visual materials. To meet the needs of the times, I examined one of the units, "IV Understanding Humans and Cultural Phenomena", among 7 authorized 'Society Culture' textbooks, so that there are 123 illustrations and 342 pictures that the percentage of them was 35.65% totally in the unit. On the examination of the frequency of using contents in each category, the illustrations are used 59 times (47.96%) in the research activity and the pictures 145 times (42.4%) in the context, which are most frequently used. Also on the examination into the actual states using contents among 'Society Culture' teachers by in-depth interviewing, they often use them when their class starts. And they require more increase in the illustrations than the pictures for easy and clear understanding and need more contents offering in the research activity to help students to study more interesting. Finally, on the result of the analysis of contents used in textbooks, exemplary cases were available to convey enough information without reading the context in the textbook because the proposed illustrations expressed the research activity's subject and the context's subject effectively. Even more, one illustration was able to indicate the sub-unit's subject while also presenting the content to be learned in the unit. However, improper cases included illustrations which are somewhat unrealistic or difficult to understand. Further, there are also some illustrations which are not related to the context. If these points are revised in the future, textbooks would be better.

• Journal of Distribution Research
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• v.17 no.3
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• pp.25-57
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• 2012

This study identified motives for writing apparel product reviews in online stores, and determined what motives increase the behavior of writing reviews. It also classified store customers based on the type of writing motives, and clarified the characteristics of internet purchase behavior and of a demographic profile. Data were collected from 252 females aged 20s' and 30s' who have experience of reading and writing reviews on online shopping. The five types of writing motives were altruistic information sharing, remedying of a grievance and vengeance, economic incentives, helping new product development, and the expression of satisfaction feelings. Among five motives, altruistic information sharing, economic incentives, and helping new product development stimulate writing reviews. Store customers who write reviews were classified into three groups based on their writing motive types: Other consumer advocates(29.8%), self-interested shoppers(40.5%) and shoppers with moderate motives(29.8%). There were significant differences among three groups in writing behavior (the frequency of writing reviews, writing intent of reviews, duration of writing reviews, and frequency of online shopping) and age. Based on results, managerial implications were suggested. Long Abstract : The purpose of present study is to identify the types of writing motives on online shopping, and to clarify the motives affecting the behavior of writing reviews. This study also classifies online shoppers based on the motive types, and identifies the characteristics of the classified groups in terms of writing behavior, frequency of online shopping, and demographics. Use and Gratification Theory was adopted in this study. Qualitative research (focus group interview) and quantitative research were used. Korean women(20 to 39 years old) who reported experience with purchasing clothing online, and reading and writing reviews were selected as samples(n=252). Most of the respondents were relatively young (20-34yrs., 86.1%,), single (61.1%), employed(61.1%) and residents living in big cities(50.9%). About 69.8% of respondents read and 40.5% write apparel reviews frequently or very frequently. 24.6% of the respondents indicated an "average" in their writing frequency. Based on the qualitative result of focus group interviews and previous studies on motives for online community activities, measurement items of motives for writing after-purchase reviews were developed. All items were used a five-point Likert scale with endpoints 1 (strongly disagree) and 5 (strongly agree). The degree of writing behavior was measured by items concerning experience of writing reviews, frequency of writing reviews, amount of writing reviews, and intention of writing reviews. A five-point scale(strongly disagree-strongly agree) was employed. SPSS 18.0 was used for exploratory factor analysis, K-means cluster analysis, one-way ANOVA(Scheffe test) and ${\chi}^2$-test. Confirmatory factor analysis and path model analysis were conducted by AMOS 18.0. By conducting principal components factor analysis (varimax rotation, extracting factors with eigenvalues above 1.0) on the measurement items, five factors were identified: Altruistic information sharing, remedying of a grievance and vengeance, economic incentives, helping new product development, and expression of satisfaction feelings(see Table 1). The measurement model including these final items was analyzed by confirmatory factor analysis. The measurement model had good fit indices(GFI=.918, AGFI=.884, RMR=.070, RMSEA=.054, TLI=.941) except for the probability value associated with the ${\chi}^2$ test(${\chi}^2$=189.078, df=109, p=.00). Convergent validities of all variables were confirmed using composite reliability. All SMC values were found to be lower than AVEs confirming discriminant validity. The path model's goodness-of-fit was greater than the recommended limits based on several indices(GFI=.905, AGFI=.872, RMR=.070, RMSEA=.052, TLI=.935; ${\chi}^2$=260.433, df=155, p=.00). Table 2 shows that motives of altruistic information sharing, economic incentives and helping new product development significantly increased the degree of writing product reviews of online shopping. In particular, the effect of altruistic information sharing and pursuit of economic incentives on the behavior of writing reviews were larger than the effect of helping new product development. As shown in table 3, online store shoppers were classified into three groups: Other consumer advocates (29.8%), self-interested shoppers (40.5%), and moderate shoppers (29.8%). There were significant differences among the three groups in the degree of writing reviews (experience of writing reviews, frequency of writing reviews, amount of writing reviews, intention of writing reviews, and duration of writing reviews, frequency of online shopping) and age. For five aspects of writing behavior, the group of other consumer advocates who is mainly comprised of 20s had higher scores than the other two groups. There were not any significant differences between self-interested group and moderate group regarding writing behavior and demographics.

• Korean Security Journal
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• no.40
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• pp.35-56
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• 2014

The purpose of this study was aimed to identify the relationship between pursuit benefit and behavior by spectators participating security exhibition. The result of study is significant because it may provide more effective and aggressive marketing strategies to the future companies participating security exhibition, and suggest developmental direction by actively responding spectators' needs. The subject for this study was spectators who participating World Security Expo 2014 held three days from March 12 to 14 in 2014. 300 samples were selected by convenience sampling for subject of this study. 283 out of 300 surveys, excluded 17 unfaithful and defected surveys, were used for data analysis. Research tool was questionnaire which was based on and recomposed by previous researches home and abroad. The collected data were treated for analysis of frequency, reliability, factor analysis, correlation, and regression by using SPSS statistic package version of 18.0. Through the above research method and procedure, the results were as followings. First, the relationship between pursuit benefit and behavior after participating exhibition appeared positively. It was found that there was high relationship between pursuit benefit and behavior. Second, analyzing relationship of factors between pursuit benefit and behaviors resulted to effect information exploration, good use of spare time, and product purchase on word of mouth. Third, analyzing relationship of factors between pursuit benefit and behaviors resulted to effect good use of spare time, information exploration, and product purchase on re-participation.

• The Journal of the Acoustical Society of Korea
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• v.37 no.5
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• pp.369-377
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• 2018

This study investigated the effect of noise and reverberation on subjective measure of speech transmission performance for elderly person with hearing loss in residential space through listening test. Floor impact, road traffic, airborne, and drainage noise were employed as the residential noise, and several impulse responses were obtained through room acoustical computer simulation for an apartment building. Sound sources for the listening test consisted of residential noises and speech sounds for boh the young (the original sound) and the aged (the sound filtered out by filters with frequency responses of hearing loss of 65 years elderly person). In the listening test, subjects evaluated speech intelligibility and listening difficulty for the presented word ($L_{Aeq}$ 55 dB) at three noise levels ($L_{Aeq}$ 30, 40, 50 dB) and three reverberation times (0.5, 1.0, 1.5 s). Results showed that the residential space with noise level lower than equal to 50 dB ($L_{i,Fmax,AW}$) for jumping noise and 40 dB ($L_{Aeq}$) for road traffic, airborne, and drainage noise had speech intelligibility of 90 % and over and listening difficulty of 30 % and below. Speech intelligibility and listening difficulty for the aged sound source was shown to be 0 % ~ 5 % lower and 2 % ~ 20 % higher than those for the young sound source, respectively.

• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.25-33
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• 2020

In this paper, the purpose is to verify the impact of performance expectations, effort expectations, social impact, individual innovation and perceived value on the intent of use and the behavior of use. Used Unified Theory of Acceptance and Use of Technology (UTAUT) to verify the applicability of this model in China, and established the research model by adding two new variables to UTAUT according to the situation of the Chinese market. To achieve this goal, 345 questionnaires were collected for experienced music creators using artificial intelligence nuggets in China by means of Internet research. The collected data were analyzed through frequency analysis, factor analysis, reliability analysis, and structural equation analysis through SPSS V. 22.0 and AMOS V 22.0. The verification of the hypotheses presented in the research model identified the decisive influence factors on the use of artificial intelligence music acceptance by Chinese users. The study is innovative in that it attempts to verify the applicability of UTAUT in the Chinese context. In the construction of the user acceptance model of AI music, three influencing factors will have an effect on users' intentions, and according to the degree of effect, from largest to smallest, they are respectively Perceived Innovativeness, Performance Expectancy and Effort Expectancy. This paper will also provide some management advices, i.e. improving the utility and usability of AI music, encouraging users with individual innovativeness, developing competitive and attractive pricing policies, increasing publicity, and prioritizing word-of-mouth advertising.

• Korean Journal of Cognitive Science
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• v.27 no.1
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• pp.129-157
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• 2016

This study investigates speech perception in noise (SPIN) in Korean. A new type of Korean SPIN test was developed by adopting a similar format to the English SPIN test. The predictability effects, noise effects and their interactions were examined in order to verify the previous findings based on English. The data from 14 Korean adults collected with this new type of Korean SPIN test confirmed the previous findings: first, the participants' overall performance was better in low noise conditions than in high noise conditions. Secondly, there was a tendency for highly predictable words to be more accurately perceived than less predictable words especially in high noise conditions. The results were interpreted in such a way that the listeners actively used both types of information: acoustic information and contextual information in speech perception. When the acoustic property of the speech sound was degraded with noise, the listeners took advantage of the linguistic contextual information in their processing of the speech sound. The findings of this study conform to those of the previous studies based on the English SPIN test. In addition, a possible effect of the frequency of target word was also found, calling for further investigation in this field of research in Korean. Implications of the results were also discussed. (Cyber Hankuk University of Foreign Studies)

• Trans-
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• v.1
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• pp.21-44
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• 2016

Advertisement is composed with symbol and sign with messages trying to express. Especially, ad with dancer introduces goods or meaning of contents with the motion of dance. In this, contents of dance or motion of dancer contains symbol and sign, understanding how ad and dance are expressed meanings with which symbol and the symbolic meaning of dance or dancer on ad. To that end, this study is for analyzing expressed symbol with dance corresponds with the aim of ad and finding the way or attitude of how normal people accept dance by reevaluating symbolic meaning of dance itself. In this study, advertisement producer and director's related data is secured for understanding direction and intention of producer, and previous study related with the study purpose, image, and effect are analyzed for understanding image of dance as a physical sign on TV advertisement. With data from www.TVCF.co.kr. TV advertisement analysis is conducted only with four ads in 2008(Nam Kwang Eng. & Const Co., Lotte Dept. Store(premium sale/gift card), Hyundai Motor Company Santa Fe -Pilobolus) and one ad in 2011(PNS The zone Sash Italy Arena di Verona when dance was used for advertisement with the highest frequency per year. Also, based on considered important factors from repeatedly watching each advertisement, scenes where movement or motion of dancer and screen word is greatly changed are analyzed as a priority. Image analysis of dance is conducted with structure studies based on physical image(line, costume, expression) and dan image(type motion, qualitative feature, mood of dance). As a result, the symbolic dance image appeared in TV advertisement can be discussed as follows. First, symbol and sign of dance on advertisement corresponds with material objects of advertisement. For instance, on the TV advertisement where Lee Youngwoo appeared, his motion as a signifer means challenge for the future of Nam Kwang Eng. & Const Co., with fast turn, jump, assemble turning jump, and sliding. Second, physical image of dancer depending on intention of sender corresponds in general, but there are somewhat differences in image of dance. This makes people to unconsciously recognize symbolic image of dance on TV ad while they watch it at the same time. Especially, when it comes to advertisement, it exposes frequently with broadcasting of organized programs from a broadcaster, living long-time memory. It can be differ based on idea and character of each of receiver. Advertisement is a medium making people naturally adopt cultural art for ordinary people in their lives. Broadcasting public art from TV advertisement widely exposes pure art to people, which was only avaliable for minority, sublimating it as an art of public culture.

• Journal of Internet of Things and Convergence
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• v.9 no.2
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• pp.19-31
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• 2023

The importance of barrier free is being highlighted worldwide. This study attempted to identify barrier-free research trends using text mining. Through this, it was intended to help with research and policies to create a barrier free environment. The analysis data is 227 papers published in domestic academic journals from 1996 when barrier free research began to 2022. The researcher converted the title, keywords, and abstract of an academic thesis into text, and then analyzed the pattern of the thesis and the meaning of the data. The summary of the research results is as follows. First, barrier-free research began to increase after 2009, with an annual average of 17.1 papers being published. This is related to the implementation guidelines for the barrier-free certification system that took effect on July 15, 2008. Second, results of barrier-free text mining i) As a result of word frequency analysis of top keywords, important keywords such as barrier free, disabled, design, universal design, access, elderly, certification, improvement, evaluation, and space, facility, and environment were searched. ii) As a result of TD-IDF analysis, the main keywords were universal design, design, certification, house, access, elderly, installation, disabled, park, evaluation, architecture, and space. iii) As a result of N-Ggam analysis, barrier free+certification, barrier free+design, barrier free+barrier free, elderly+disabled, disabled+elderly, disabled+convenience facilities, the disabled+the elderly, society+the elderly, convenience facilities+installation, certification+evaluation index, physical+environment, life+quality, etc. appeared in a related language. Third, as a result of the CONCOR analysis, cluster 1 was barrier-free issues and challenges, cluster 2 was universal design and space utilization, cluster 3 was Improving Accessibility for the Disabled, and cluster 4 was barrier free certification and evaluation. Based on the analysis results, this study presented policy implications for vitalizing barrier-free research and establishing a desirable barrier free environment.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.