• Information Systems Review
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• v.16 no.3
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• pp.49-68
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• 2014

These days, consumers have increasingly preferred to digital real-time streamlining and downloading to listen to music because this is convenient and affordable for the consumers. Accordingly, sales of music in compact disk formats have steadily declined. In this regards, online digital music has become a new communication channel to listen musics, where digital files can be delivered over various online networks to people's computing devices. The majority of online digital music distributors has Music Recommender Systems for sales of digital music on their websites. Music Recommender Systems are parts of information filtering systems that provide the ratings or preferences that users give to music. Korean online digital music distributors have Music Recommender Systems. But those online music distributors didn't provide any rules or clear procedures that recommend music. Therefore, we raise important questions as follows: "Is Music Recommender Systems Fair?", "What is the impact of Music Recommender Systems on online music rankings and sales?" While previous studies have focused on usefulness of Music Recommender Systems, this study investigates not only fairness of Current Music Recommender Systems but also Relationship between Music Recommender Systems and online Music Charts. This study examines these issues based on Bandwagon effect, ranking effect, Slot effect theories. For our empirical analysis, we selected the most famous five online digital music distributors in terms of market shares. We found that all recommended music is exposed to the top of 'daily music charts' in online digital music distributors' websites. We collected music ranking data and recommended music data from 'daily music chart' during a one month. The result shows that online music recommender systems are not fair, since they mainly recommend particular music that supported by a specific music production company. In addition, the recommended music are always exposed to the top of music ranking charts. We also find that recommended music usually appear at the top 20 ranking charts within one or two days. Also, the most music in the top 50 or 100 ranks are the recommended music. Moreover, recommended music usually remain the ranking charts more than one month while non-recommended music often disappear at the ranking charts within two week. Our study provides an important implication to online music industry. Because music recommender systems and music ranking charts are closely related, music distributors may improperly use their recommender systems to boost the sales of music that related to their own companies. Therefore, online digital music distributor must clearly announce the rules and procedures about music recommender systems for the better music industry.

• Horticultural Science & Technology
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• v.32 no.6
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• pp.887-900
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• 2014

This study was conducted to provide a job analysis for, and assess the job performance of horticultural therapists, as well as examine future educational needs. To this end, a chart developed using the DACUM method was chosen as the appropriate tool for the job analysis of horticultural therapists (Study 1). Based on the chart, a survey using an evaluation form was produced to investigate the current level of job performance and future required level of horticultural therapists (Study 2). A total of 8 duties and 45 tasks were classified to examine job performance, based on analysis of the DACUM Council (Study 1). These duties include A. Decide execution organization for horticultural therapy (HT) program, B. Diagnose and assess clients before starting the HT program, C. Plan HT program, D. Develop HT program, E. Prepare to implement HT program for each session, F. Implement HT program for each session, G. Implement overall assessment for HT program, and H. Develop oneself as a horticultural therapist. Their duties were broken down further into five to eight tasks per duty, totaling 45 tasks. Based on the horticultural therapist job performance sheet developed through this process, an assessment of the current job level of horticultural therapists was performed and future required level were examined (Study 2). The evaluation forms were sent to 779 horticultural therapists with level 1 or 2 certification via email or mail delivery. The analysis of 242 questionnaires (31.1%) revealed that horticultural therapists with level 1 certificates have a significantly higher job performance level for 34 of the 45 tasks. Regarding future required level, 20 out of 45 tasks were assessed as higher for level 1 horticultural therapists than level 2. In addition, a Borich formula was utilized to identify the priority of educational needs for the 45 horticultural therapist tasks. The results revealed the following top three tasks: H1. Receive feedback from the supervisor for the horticultural therapy program; A1. Distribute promotional materials about the horticultural therapy program; and H2. Submit a grant proposal for horticultural therapy program to organizations such as welfare foundations. The results of this study are anticipated to facilitate understanding and improve work conditions for current horticultural therapists or horticultural therapists-in-training. In addition, institutions that train horticultural therapists will be able to use this as basic research to develop a practical training curriculum.

• Korean Journal of Soil Science and Fertilizer
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• v.34 no.5
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• pp.316-325
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• 2001

To develope a technique for efficiently managing fertilizer for cucumber, a quick test method to quantify nitrate content in soil solution and leaf petiole juice using a simple instrument was investigated. Among the nitrate analyzing instruments such as compact ion meter, nitrate ion meter, and test strip with reflectometer, the paper test-strip used in conjunction with a hand-held reflectometer was most closely correlated with ion chromatography method in nitrate content, and then it would be suggested with a tool that a farmer can use rapidly, conveniently and accurately for nitrate analysis in a field. Nitrate content in soil solution collected by porous cup was very variable on the lapsed time after drip irrigation and the sampling positions such as soil depth and the distance from dripper. As a result, a significant correlation between nitrate contents of soil solutions and 2M KCl soil extract was not found. However, nitrate content in soil solution extracted with a volume basis (soil:water=1:2) showed the highly significant correlation with that in 2M KCl extract. Nitrate contents of cucumber leaf petiole juices was greatly different between upper and lower leaves. Eleven to sixteen positioned-leaf would be a proper sampling position to determine nitrate content in leaf petiole for evaluating nutrient state by plant tissue analysis. From the secondary regression equations between nitrate contents of soil and petiole juice and the yield of cucumber, nitrate levels for real time diagnosis were estimated as $400mg\;l^{-1}$ soil solution by porous cup. $300mg\;l^{-1}$ in a soil volume extraction, and $1400mg\;l^{-1}$ in petiole juice from spring to summer season. In addition, the maximum yield of cucumber fruit in pot test was obtained in nitrate $1500mg\;l^{-1}$ level of petiole juice, which was similar to nitrate $1400mg\;l^{-1}$ in greenhouse trial.

• Weed & Turfgrass Science
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• v.4 no.3
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• pp.159-175
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• 2015

Surveys of weed species on upland fields were conducted in Korea to investigate the occurrence of weed flora from April to May 2014 for winter crop fields and from July to August 2014 for summer crop fields. From the nation-wide survey, 375 weed species in 50 families were identified and classified to 162 annuals, 78 biennials and 135 perennials. Based on the occurrence ratio, the most weed species belonged to Compositae (73 species). 44 and 25 weed species belonged to Poaceae and Polygonaceae, respectively, and these 183 weed species in the most five families accounted for 49% of total weed occurrence. While 287 weed species in 45 families occurred in the winter crop fields, 339 weed species in 47 families occurred in summer crop fields. The most dominant weed species in Korean upland fields were Digitaria ciliaris, followed by Portulaca oleracea, Acalypha australis, Chenopodium album, Rorippa palustris etc. 129 weed species in 25 families were considered as exotic weeds. Based on the importance analysis, the highest value was C. album followed by Amaranthus lividus, Conyza canadensis etc. This information could be useful for estimation of future weed occurrence, weed population dynamics and establishment of weed control methods in upland fields of Korea.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.19 no.1
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• pp.66-75
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• 2014

This study aims to understand environmental factors that determine spatial distribution of macrozoobenthic community in the southern area (ca 100-500 m depth) of East Sea, Korea, known as a candidate site for carbon storage under the seabed. From sixteen locations sampled in the summer of 2012, a total of 158 species were identified, showing density of $843indiv/m^2$ and biomass of $26.2g\;WW/m^2$, with increasing faunal density towards biologically higher diverse locations. Principal component analysis showed that a total of 33 environmental parameters were reduced to three principal components (PC), indicating sediment, bottom water, and depth, respectively. As sand content was increasing, number of species increased but biomass decreased. Six dominant species including two bivalve species favored high concentrations of ${\Omega}$ aragonite and ${\Omega}$ calcite, indicating that the corresponding species can be severely damaged by ocean acidification or $CO_2$ effluent. Cluaster analysis based on more than 1% density dominant species classified the entire study area into four faunal assemblage (location groups), which were delineated by characteristic species, including (A) Ampelisca miharaensis, (B) Edwardsioides japonica, (C) Maldane cristata, (D) Spiophanes kroeyeri, and clearly separated in terms of geography, bottom water and sediment environment. Overall, a discriminant function model was developed to predict four faunal assemblages from five simply-measured environmental variables (depth, sand content in sediment, temperature, salinity and pH in bottom water) with 100% accuracy, implying that benthic faunal assemablages are closed linked to certain combinations of abiotic factors.

• Cartoon and Animation Studies
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• s.41
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• pp.253-277
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• 2015

When was launched in 2014, it was immediately ranked 2nd in English gross sales record in the first half of 2014, attracting huge attention from avid game users. Colin Moriarty(2014) states that the worldwide popular game, , lies on the alternative history assumption, 'what if Nazi Germany won the second world war? In regard to this, this research addresses how the characteristics of alternative history was adopted and visually represented in FPS game trailer. In terms of research method, firstly, literatures will be reviewed about definition of alternative history and some of the previous examples where alternative history was applied in novels, films and games. Secondly, narrative composition of alternative history is categorized as three sequential phases, (1) borrowing real history material, (2) connection between real and fictional history and (3) reconstruction of history through reinterpretation. Thirdly, the live-action game trailer will be analysed by three sequential phases of narrative composition, and CG game character and background will be analysed by spatial background, characters and props. The phase of 'borrowing' has used the historic images related to the World War II, and the phase of "connection' has composited by "connection through circumstantial events". The phase of 'reconstruction' has unfolded its fictional narrative in the form of "limited fictional history" In addition to this, has constructed dystopia world through composing of historic images and CG characters by SF design. In the light of this, the narrative composition of alternative history successfully extends to game area.

• Journal of Global Scholars of Marketing Science
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• v.18 no.1
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• pp.141-156
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• 2008

Market structure analysis is a very useful tool to analyze the competition boundary of the brand or the company. But most of the studies in market structure analysis, the concern lies in nondurable goods such as candies, soft drink and etc. because of the their availability of the data. In the field of durable goods, the limitation of the data availability and the repurchase time period constrain the study. In the analysis of the automobile market, those of views might be more persuasive. The purpose of this study is to analyze the structure of automobile market based on some idea suggested by prior studies. Usually the buyers of the automobile tend to buy upper tier when they buy in the next time. That kind of behavior make it impossible to analyze the structure of automobile market under the level of automobile model. For that reason I tried to analyze the market structure in the brand or company level. In this study, consideration data was used for market structure analysis. The reasons why we used the consideration data are summarized as following. Firstly, as the repurchase time cycle is too long, brand switching data which is used for the market analysis of nondurable good is not avaliable. Secondly, as we mentioned, the buyers of the automobile tend to buy upper tier when they buy in the next time. We used survey data collected in the U.S.A. market in the year of 2005 through questionaire. The sample size was 8,291. The number of brand analyzed in this study was 9 among 37 which was being sold in U.S.A. market. Their market share was around 50%. The brands considered were BMW, Chevrolet, Chrysler, Dodge, Ford, Honda, Mercedes, and Toyota. �� ratio was derived from frequency of the consideration set. Actually the frequency is different from the brand switch concept. In this study to compute the �� ratio, the frequency of the consideration set was used like a frequency of brand switch for convenience. The study can be divided into 2 steps. The first step is to build hypothetical market structures. The second step is to choose the best structure based on the hypothetical market structures, Usually logit analysis is used for the choice best structure. In this study we built 3 hypothetical market structure. They are type-cost, cost-type, and unstructured. We classified the automobile into 5 types, sedan, SUV(Sport Utility Vehicle), Pickup, Mini Van, and Full-size Van. As for purchasing cost, we classified it 2 groups based on the median value. The median value was $28,800. To decide best structure among them, maximum likelihood test was used. Resulting from market structure analysis, we find that the automobile market of USA is hierarchically structured in the form of 'automobile type - purchasing cost'. That is, result showed that automobile buyers considered function or usage first and purchasing cost next. This study has some limitations in the analysis level and variable selection. First, in this study only type of the automobile and purchasing cost were as attributes considered for purchase. Considering other attributes is very needful. Because of the attributes considered, only 3 hypothetical structure could be analyzed. Second, due to the data, brand level analysis was tried. But model level analysis would be better because automobile buyers consider model not brand. To conduct model level study more cases should be obtained. That is for acquiring the better practical meaning, brand level analysis should be conducted when we consider the actual competition which occurred in the real market. Third, the variable selection for building nested logit model was very limited to some avaliable data. In spite of those limitations, the importance of this study lies in the trial of market structure analysis of durable good.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• Journal of Technology Innovation
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• v.17 no.2
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• pp.55-80
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• 2009

Technology innovation activity plays a pivotal role in constructing the entrance barrier for other firms and making process improvement and new product. and these activities give a profit increase and growth to firms. Thus, technology innovation activity can reduce the default risk of firms. However, technology innovation activity can also increase the firm's default risk because technology innovation activity requires too much investment of the firm's resources and has the uncertainty on success. The purpose of this study is to examine the effect of technology innovation activity on the default risk of firms. This study's sample consists of manufacturing firms listed on the Korea Securities Market and The Kosdaq Market from January 1,2000 to December 31, 2008. This study makes use of R&D intensity as an proxy variable of technology innovation activity. The default probability which proxies the default risk of firms is measured by the Merton's(l974) debt pricing model. The main empirical results are as follows. First, from the empirical results, it is found that technology innovation activity has a negative and significant effect on the default risk of firms independent of the Korea Securities Market and Kosdaq Market. In other words, technology innovation activity reduces the default risk of firms. Second, technology innovation activity reduces the default risk of firms independent of firm size, firm age, and credit score. Third, the results of robust analysis also show that technology innovation activity is the important factor which decreases the default risk of firms. These results imply that a manager must show continuous interest and investment in technology innovation activity of one's firm. And a policymaker also need design an economic policy to promote the technology innovation activity of firms.

• The Korean Journal of Petroleum Geology
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• v.3 no.1 s.4
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• pp.1-15
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• 1995

Seismic reflection profiles and exploratory drilling well samples from the southern marginal-continental shelf basin of Korea delineate that the Tertiary sedimentary sequences can be grouped into five sequences (Sequence A, Sequence B, Sequence C, Sequence D and Sequence E, in descending order). Paleontologic data, K-Ar age datings, correlation with tuff layers and sequence stratigraphic analysis reveal that the sequences A, B, C, D and E can be considered as the deposits of Holocene $\~$ Pleistocene, Pliocene, Late Miocene, Early $\~$ Middle Miocene and Oligocene, respectively. The sequence stratigraphic and structural analyses suggest that the southern part of the Cheju Basin had experienced severe folding and faulting. NE-SW trending strike-slip movement is responsible for the deformation. The sinistral movement of strike-slip fault ceased before the deposition of Sequence B. Age dating and rare-earth elements analysis of volvanic rocks reveal+ that the Sequence D was deposited during the Early $\~$ Middle Miocene and the Sequence I was deposited earlier than the deposition of the Green Tuff Formation. Sedimentary petrological studies indicate that sediments of the Sequence I came from the continental block provenance. After the deposition of the Sequence E, uplift of the source area resulted in increase of sediment supply, subsidence and volcanic activities. The Sequence D show these factors and the sediments of the Sequence D are considered to be transported from the recycled orogenic belt.