• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.31 no.3
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• pp.61-74
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• 2013

This study examined and analyzed ancient writing and poetry regarding cases of appreciating plants by using pots in a garden of a palace or private houses by ancestors, and examined shape and planting method of plant species and potted plants, arrangement and preference of potted plants. As for the method of the study, description research method which examines and interprets poem and painting based on potted plants. The results of this study is summarized like the following. First, the plants which were favorably used for potted plants include 19 kinds such as Prunus mume, Pinus densiflora, Pinus pumila, Phyllostachys spp., Camellia japonica, Punica granatum, and Gardenia jasminoidesa, and as for herbs, 12 kinds such as Chrysanthemum monifolium and Nelumbo nusifera . Second, the species which were specially arranged into artificial shapes include Prunus mume and Pinus densiflora. The two plants made the shape of severe curves of stems such as Wangpi. Gyuban, and Bangan, and there are Pinus densiflora dwarfed potted plant whose roots are stretched on Prunus mume grafted into a strange stump and an oddly shaped stone. For the beauty of the dwarfed tree shape, pine cones are added to an old Pinus densiflora or Parthenocissus tricuspidata is planted to stems, and additional method of making moss on the soil, which is called 'Jongbunchuigyeong'. As for planting method, water culture, planting on a stone, planting on a charcoal, and assembled planting are expressed in poetry. Third, as for external space for potted plants, a place where a king stays, a bed room for a king, surrounding areas and gardens of private houses, and step stones were used as a space which adds artistic effects. Potted plants are placed on a table in a library, on a desk, on a drawer, and near a pillow as a small items in a room, and scholars enjoyed original characteristics and symbolism of the potted plants. Fourth, at the time of flowering of Prunus mume, poetry event was held to enjoy the tree and writing poetry begun. And at the time of flowering of Chrysanthemum monifolium, the flowers were floated in a liquor glass or shadow play was enjoyed. Fifth, potted plants played the role of garden ornaments in elegant events of a palace, the gentry, wedding ceremony, and sacrificial rites. Sixth, potted plants were used as tributes between countries, donation to a king, or a gift of a king. In addition, there were many cases where scholars exchanged potted plants and there is the first record of giving a potted plant in 'Mokeunsigo' by Mokeun Isaek, scholar in the late era of Goryeo. Seventh, at the time of flowering Prunus mume, Chrysanthemum monifolium, Gardenia jasminoides, Nelumbo nusifera, and Narcissustazetta var. chinensis, they enjoyed the particular fragrance and express it into poetry. Eighth, plant species from southern parts such as Camellia japonica, Daphne odora, Gardenia jasminoides, Citrus unshiu, Phyllostachys spp., Punica granatum, Rosa rugosa, and Musa basjoo, or foreign plant species, and species weak against the cold were utilized as pot plants for enjoying green trees indoors in northern central province in harshly cold winter.

• Korean Journal of Environmental Agriculture
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• v.36 no.2
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• pp.87-96
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• 2017

BACKGROUND: The objective of this study was to identify and compare the main phenolic compounds (anthocyanins, flavonoids, phenolic acids) in blueberry and black raspberry cultivated in Korea using ultra-performance liquid chromatography diode array detection-quadrupole time-of-flight mass spectrometry (UPLC-DAD-QTOF/MS). METHODS AND RESULTS: Twenty-nine flavonoids were identified by comparison of ultraviolet and mass spectra with data in a chemical library and published data. Blueberry contained flavonols including kaempferol, quercetin, isorhamnetin, myricetin, and syringetin aglycones. Isorhamnetin 3-O-robinobioside, kaempferol 3-O-(6"-O-acetyl)glucoside, quercetin, quercetin 3-O-arabinofuranoside (avicularin), quercetin 3-O-(6''-O-malonyl) glucoside, and quercetin 3-O-robinobioside were detected for the first time in blueberry. The flavonoids in raspberry consisted of quercetin aglycone and its glycosides. The mean total flavonoid content in blueberry [143.0 mg/100 g dry weight (DW)] was 1.5-times that in raspberry (95.4 mg/100 g DW). The most abundant flavonoid in blueberry was quercetin 3-O-galactoside (hyperoside, up to 76.1 mg/100 g DW) and that in raspberry was quercetin 3-O-glucuronide (miquelianin, up to 55.5 mg/100 g DW). Miquelianin was not detected in blueberry. CONCLUSION: Flavonol glycosides were the main flavonoids in blueberry and black raspberry cultivated in Korea. The composition and contents of flavonoids differed between blueberry and black raspberry, and may be affected by the cultivar and cultivation conditions.

• Journal of Christian Education in Korea
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• v.71
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• pp.181-210
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• 2022

The purpose of this study was to investigate the research trends related to the children's department of church schools. The purpose of this study is to present basic data for the study of the children's department of church schools by analyzing the research period, research contents, research methods, and subjects of research related to the children's department of church schools. For this study, 50 domestic master's and doctoral dissertations searched through the National Assembly Library and the Research Information Sharing Service(RISS) were extracted with the keywords of 'church school' and 'children's department'. The frequency and percentage were calculated by analyzing the research related to the children's department of the church school according to four criteria: research period, research content, research method, and research subject. As a result of the study, first, the research trend of research papers in the children's department of church schools was found to be 49 articles (98%) for master's degrees and 1 article (2%) for doctoral degrees from 1980 to 2022. Trends by research period are focused on master's degrees. Second, the trend by research content was 27 practical studies (54%) and 23 theory studies (46%). In the research related to the children's department of church schools, the practical research accounted for a relatively high percentage compared to the theory research. Third, the trends by research method were in the order of 30 literature studies (60%), 19 quantitative studies (38%), and 1 qualitative study (2%). Research related to children's departments in church schools is being actively conducted with a focus on literature research. Fourth, as for the trends by study subject, the study was conducted focusing on physical subjects, with 35 subjects (70%) and 15 subjects (30%) of personal subjects. As research is conducted from physical objects to church schools and media, it is necessary to study the connection between church schools and families. As the research on church school kindergarten is focused on adults (teachers, parents, and educational preachers), in-depth research on children in church schools and qualitative research with voices from the field of children's department in church schools are required.

• Journal of Christian Education in Korea
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• v.68
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• pp.245-277
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• 2021

The purpose of this study was to investigate the trend of Christian picture book-related research. The purpose of this study is to present basic data for various and balanced research and development in the Christian picture book field by analyzing the research period, research content, and research method related to Christian picture books. For this study, 45 domestic master's and doctoral dissertations were extracted through the National Assembly Library and the Academic Research Information Service (RISS) with the keywords of 'Christian picture book', 'Bible picture book', 'Christian story', and 'Bible story'. The frequency and percentage were calculated by analyzing Christian picture book-related studies according to four criteria: research period, research content, research method, and research subject. As a result of the study, first, the trend of Christian picture book research papers by research period from 1999 to 2021 was 43 master's articles (95.6%) and 2 doctoral articles (4.4%), focusing on Christian picture book-related studies. Second, the trend by research content was found to be 12 basic studies (26.6%) and 33 practical studies (73.4%). Research related to Christian picture books is being actively conducted focusing on practical research rather than basic research. Third, the trend by research method was in the order of 33 quantitative studies (73.4%), 11 literature studies (24.4%), and 1 qualitative study (2.2%). Research related to Christian picture books is centered on quantitative research, and literature research and qualitative research account for a relatively low proportion. Fourth, as for the trends by study subject, there were 35 human subjects (77.8%) and 10 physical subjects (22.2%). Among human subjects, 33 single subjects (73.4%) and 2 mixed subjects (4.4%) were found, and among single subjects, 30 studies (66.7%) targeting children were high. In other words, research on Christian picture books had a higher proportion of studies with children as a single subject than mixed subjects between children and children, children and teachers, and between children and parents.

• Journal of Christian Education in Korea
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• v.69
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• pp.77-104
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• 2022

The purpose of this study was to investigate the research trend of forest play. The purpose of this study is to provide basic data for the vitalization of forest play research by analyzing the research period, research content, and research methods. For this study, 57 domestic master's and doctoral dissertations were extracted through the National Assembly Library and the Research Information Sharing Service(RISS) with the keywords of 'forest', 'play', and 'forest play'. The frequency and percentage were calculated by analyzing forest play research based on four criteria: research period, research content, research method, and research subject. As a result of the research, first, the trend of forest play research by period is from 2011 to 2021, with 49 articles (85.9%) for master's degrees and 8 articles (14.1%) for doctor's degrees. Second, the trend by research content was found to be 16 basic studies (28.1%) and 41 practical studies (71.9%). Forest play research is being actively conducted centered on practical research. Third, the trends by research method were in the order of 39 quantitative studies (68.4%), 17 qualitative studies (29.8%), and 1 literature study (1.8%). Forest play research is focused on quantitative research, and comparatively qualitative research and literature research account for a low proportion. Fourth, the trend by study subject was 56 single subject studies (98.2%). The single subjects were 52 children (91.2%), 3 teachers (5.2%), and 1 parent (1.8%). As for the mixed subjects, there is one study (1.8%) targeting children and parents, and it is necessary to conduct a study with mixed subjects. As for the study of material subjects, 42 articles (73.7%) in the natural environment, 13 articles (22.8%) in educational institutions, and 2 articles (3.5%) in the media were found in the order. Research on the home environment related to forest play is insufficient, so research on parents, children-parents, and home environment related to forest play should be conducted in the future.

• Journal of the Daesoon Academy of Sciences
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• v.27
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• pp.201-243
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• 2016

This paper analyzes the research trends from 358 scholarly articles published in the Journal of Daesoon Academy of Sciences from the first published journal in 1996 to the most recent journal published on the 25^th of 2015 and proposes ideas for improvement. First of all, "The Journal of Daesoon Academy of Sciences" does not meet the standards required by the National Research Foundation, falling short of the most important conditions for the registration such as the periodicity and punctuality expected from academic journals. Furthermore, in terms of the Bibliometrical analysis, the number of articles published by the journal is decreasing and the consistency, with regards to rules and principles regulating publication details and bibliography formats, is nonexistent. Although various authors seemed to be meeting these criteria on the surface, the ratio of co-authored articles is too small. Securing researchers specializing in Daesoon Thought for expanding the size of the journal is important, but it is also important to diversify the research topics through exchanging ideas among researchers from various organizations. Here are some ideas for the improvement of the Journal of Daesoon Academy of Sciences: First, in order to meet the standards for punctuality and periodicity, it would be best to publish the journal twice a year with 12 to 15 articles. Second, the journal must become searchable through the creation of a database. Third, the key words and abstracts of articles must be written in Korean and English to facilitate the sharing of articles among researchers. Fourth, the journal must have a diverse and outstanding editorial board which takes into account the geographical situations of its board members. Fifth, the Journal must include articles on relevant topics that reflect the core topics of the Daesoon Thought and other studies. Sixth, articles must have a front page that contains bibliographical items to convey information to the reader. Seventh, it is essential that the journal have a clear publication date detailing the year, month, and day as well as a standard numbering scheme (i.e, Vol. and no).

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Asia pacific journal of information systems
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• v.21 no.1
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• pp.103-122
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• 2011

Recently, numerous documents have been made available electronically. Internet search engines and digital libraries commonly return query results containing hundreds or even thousands of documents. In this situation, it is virtually impossible for users to examine complete documents to determine whether they might be useful for them. For this reason, some on-line documents are accompanied by a list of keywords specified by the authors in an effort to guide the users by facilitating the filtering process. In this way, a set of keywords is often considered a condensed version of the whole document and therefore plays an important role for document retrieval, Web page retrieval, document clustering, summarization, text mining, and so on. Since many academic journals ask the authors to provide a list of five or six keywords on the first page of an article, keywords are most familiar in the context of journal articles. However, many other types of documents could not benefit from the use of keywords, including Web pages, email messages, news reports, magazine articles, and business papers. Although the potential benefit is large, the implementation itself is the obstacle; manually assigning keywords to all documents is a daunting task, or even impractical in that it is extremely tedious and time-consuming requiring a certain level of domain knowledge. Therefore, it is highly desirable to automate the keyword generation process. There are mainly two approaches to achieving this aim: keyword assignment approach and keyword extraction approach. Both approaches use machine learning methods and require, for training purposes, a set of documents with keywords already attached. In the former approach, there is a given set of vocabulary, and the aim is to match them to the texts. In other words, the keywords assignment approach seeks to select the words from a controlled vocabulary that best describes a document. Although this approach is domain dependent and is not easy to transfer and expand, it can generate implicit keywords that do not appear in a document. On the other hand, in the latter approach, the aim is to extract keywords with respect to their relevance in the text without prior vocabulary. In this approach, automatic keyword generation is treated as a classification task, and keywords are commonly extracted based on supervised learning techniques. Thus, keyword extraction algorithms classify candidate keywords in a document into positive or negative examples. Several systems such as Extractor and Kea were developed using keyword extraction approach. Most indicative words in a document are selected as keywords for that document and as a result, keywords extraction is limited to terms that appear in the document. Therefore, keywords extraction cannot generate implicit keywords that are not included in a document. According to the experiment results of Turney, about 64% to 90% of keywords assigned by the authors can be found in the full text of an article. Inversely, it also means that 10% to 36% of the keywords assigned by the authors do not appear in the article, which cannot be generated through keyword extraction algorithms. Our preliminary experiment result also shows that 37% of keywords assigned by the authors are not included in the full text. This is the reason why we have decided to adopt the keyword assignment approach. In this paper, we propose a new approach for automatic keyword assignment namely IVSM(Inverse Vector Space Model). The model is based on a vector space model. which is a conventional information retrieval model that represents documents and queries by vectors in a multidimensional space. IVSM generates an appropriate keyword set for a specific document by measuring the distance between the document and the keyword sets. The keyword assignment process of IVSM is as follows: (1) calculating the vector length of each keyword set based on each keyword weight; (2) preprocessing and parsing a target document that does not have keywords; (3) calculating the vector length of the target document based on the term frequency; (4) measuring the cosine similarity between each keyword set and the target document; and (5) generating keywords that have high similarity scores. Two keyword generation systems were implemented applying IVSM: IVSM system for Web-based community service and stand-alone IVSM system. Firstly, the IVSM system is implemented in a community service for sharing knowledge and opinions on current trends such as fashion, movies, social problems, and health information. The stand-alone IVSM system is dedicated to generating keywords for academic papers, and, indeed, it has been tested through a number of academic papers including those published by the Korean Association of Shipping and Logistics, the Korea Research Academy of Distribution Information, the Korea Logistics Society, the Korea Logistics Research Association, and the Korea Port Economic Association. We measured the performance of IVSM by the number of matches between the IVSM-generated keywords and the author-assigned keywords. According to our experiment, the precisions of IVSM applied to Web-based community service and academic journals were 0.75 and 0.71, respectively. The performance of both systems is much better than that of baseline systems that generate keywords based on simple probability. Also, IVSM shows comparable performance to Extractor that is a representative system of keyword extraction approach developed by Turney. As electronic documents increase, we expect that IVSM proposed in this paper can be applied to many electronic documents in Web-based community and digital library.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.183-203
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• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.

• Korean small business review
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• v.31 no.4
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• pp.67-93
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• 2009

The purpose of this paper is to explore the relations between financial constraints and dividend smoothing of innovative small and medium sized enterprises(SMEs) listed on Korea Securities Market and Kosdaq Market of Korea Exchange. The innovative SMEs is defined as the firms with high level of R&D intensity which is measured by (R&D investment/total sales) ratio, according to Chauvin and Hirschey (1993). The R&D investment plays an important role as the innovative driver that can increase the future growth opportunity and profitability of the firms. Therefore, the R&D investment have large, positive, and consistent influences on the market value of the firm. In this point of view, we expect that the innovative SMEs can adjust dividend payment faster than the noninnovative SMEs, on the ground of their future growth opportunity and profitability. And also, we expect that the financial unconstrained firms can adjust dividend payment faster than the financial constrained firms, on the ground of their financing ability of investment funds through the market accessibility. Aivazian et al.(2006) exert that the financial unconstrained firms with the high accessibility to capital market can adjust dividend payment faster than the financial constrained firms. We collect the sample firms among the total SMEs listed on Korea Securities Market and Kosdaq Market of Korea Exchange during the periods from January 1999 to December 2007 from the KIS Value Library database. The total number of firm-year observations of the total sample firms throughout the entire period is 5,544, the number of firm-year observations of the dividend firms is 2,919, and the number of firm-year observations of the non-dividend firms is 2,625. About 53%(or 2,919) of these total 5,544 observations involve firms that make a dividend payment. The dividend firms are divided into two groups according to the R&D intensity, such as the innovative SMEs with larger than median of R&D intensity and the noninnovative SMEs with smaller than median of R&D intensity. The number of firm-year observations of the innovative SMEs is 1,506, and the number of firm-year observations of the noninnovative SMEs is 1,413. Furthermore, the innovative SMEs are divided into two groups according to level of financial constraints, such as the financial unconstrained firms and the financial constrained firms. The number of firm-year observations of the former is 894, and the number of firm-year observations of the latter is 612. Although all available firm-year observations of the dividend firms are collected, deletions are made in the case of financial industries such as banks, securities company, insurance company, and other financial services company, because their capital structure and business style are widely different from the general manufacturing firms. The stock repurchase was involved in dividend payment because Grullon and Michaely (2002) examined the substitution hypothesis between dividends and stock repurchases. However, our data structure is an unbalanced panel data since there is no requirement that the firm-year observations data are all available for each firms during the entire periods from January 1999 to December 2007 from the KIS Value Library database. We firstly estimate the classic Lintner(1956) dividend adjustment model, where the decision to smooth dividend or to adopt a residual dividend policy depends on financial constraints measured by market accessibility. Lintner model indicates that firms maintain stable and long run target payout ratio, and that firms adjust partially the gap between current payout rato and target payout ratio each year. In the Lintner model, dependent variable is the current dividend per share(DPS_t), and independent variables are the past dividend per share(DPS_t-1) and the current earnings per share(EPS_t). We hypothesized that firms adjust partially the gap between the current dividend per share(DPS_t) and the target payout ratio(Ω) each year, when the past dividend per share(DPS_t-1) deviate from the target payout ratio(Ω). We secondly estimate the expansion model that extend the Lintner model by including the determinants suggested by the major theories of dividend, namely, residual dividend theory, dividend signaling theory, agency theory, catering theory, and transactions cost theory. In the expansion model, dependent variable is the current dividend per share(DPS_t), explanatory variables are the past dividend per share(DPS_t-1) and the current earnings per share(EPS_t), and control variables are the current capital expenditure ratio(CEA_t), the current leverage ratio(LEV_t), the current operating return on assets(ROA_t), the current business risk(RISK_t), the current trading volume turnover ratio(TURN_t), and the current dividend premium(DPREM_t). In these control variables, CEA_t, LEV_t, and ROA_t are the determinants suggested by the residual dividend theory and the agency theory, ROA_t and RISK_t are the determinants suggested by the dividend signaling theory, TURN_t is the determinant suggested by the transactions cost theory, and DPREM_t is the determinant suggested by the catering theory. Furthermore, we thirdly estimate the Lintner model and the expansion model by using the panel data of the financial unconstrained firms and the financial constrained firms, that are divided into two groups according to level of financial constraints. We expect that the financial unconstrained firms can adjust dividend payment faster than the financial constrained firms, because the former can finance more easily the investment funds through the market accessibility than the latter. We analyzed descriptive statistics such as mean, standard deviation, and median to delete the outliers from the panel data, conducted one way analysis of variance to check up the industry-specfic effects, and conducted difference test of firms characteristic variables between innovative SMEs and noninnovative SMEs as well as difference test of firms characteristic variables between financial unconstrained firms and financial constrained firms. We also conducted the correlation analysis and the variance inflation factors analysis to detect any multicollinearity among the independent variables. Both of the correlation coefficients and the variance inflation factors are roughly low to the extent that may be ignored the multicollinearity among the independent variables. Furthermore, we estimate both of the Lintner model and the expansion model using the panel regression analysis. We firstly test the time-specific effects and the firm-specific effects may be involved in our panel data through the Lagrange multiplier test that was proposed by Breusch and Pagan(1980), and secondly conduct Hausman test to prove that fixed effect model is fitter with our panel data than the random effect model. The main results of this study can be summarized as follows. The determinants suggested by the major theories of dividend, namely, residual dividend theory, dividend signaling theory, agency theory, catering theory, and transactions cost theory explain significantly the dividend policy of the innovative SMEs. Lintner model indicates that firms maintain stable and long run target payout ratio, and that firms adjust partially the gap between the current payout ratio and the target payout ratio each year. In the core variables of Lintner model, the past dividend per share has more effects to dividend smoothing than the current earnings per share. These results suggest that the innovative SMEs maintain stable and long run dividend policy which sustains the past dividend per share level without corporate special reasons. The main results show that dividend adjustment speed of the innovative SMEs is faster than that of the noninnovative SMEs. This means that the innovative SMEs with high level of R&D intensity can adjust dividend payment faster than the noninnovative SMEs, on the ground of their future growth opportunity and profitability. The other main results show that dividend adjustment speed of the financial unconstrained SMEs is faster than that of the financial constrained SMEs. This means that the financial unconstrained firms with high accessibility to capital market can adjust dividend payment faster than the financial constrained firms, on the ground of their financing ability of investment funds through the market accessibility. Futhermore, the other additional results show that dividend adjustment speed of the innovative SMEs classified by the Small and Medium Business Administration is faster than that of the unclassified SMEs. They are linked with various financial policies and services such as credit guaranteed service, policy fund for SMEs, venture investment fund, insurance program, and so on. In conclusion, the past dividend per share and the current earnings per share suggested by the Lintner model explain mainly dividend adjustment speed of the innovative SMEs, and also the financial constraints explain partially. Therefore, if managers can properly understand of the relations between financial constraints and dividend smoothing of innovative SMEs, they can maintain stable and long run dividend policy of the innovative SMEs through dividend smoothing. These are encouraging results for Korea government, that is, the Small and Medium Business Administration as it has implemented many policies to commit to the innovative SMEs. This paper may have a few limitations because it may be only early study about the relations between financial constraints and dividend smoothing of the innovative SMEs. Specifically, this paper may not adequately capture all of the subtle features of the innovative SMEs and the financial unconstrained SMEs. Therefore, we think that it is necessary to expand sample firms and control variables, and use more elaborate analysis methods in the future studies.