• Journal of the Korea Society of Computer and Information
• /
• v.20 no.2
• /
• pp.29-45
• /
• 2015

Systematic theory, concepts, and methodology for the biological evolution have been developed while patterns and principles of the evolution have been actively studied in the past 200 years. Furthermore, they are applied to various fields such as evolutionary economics, evolutionary psychology, evolutionary linguistics, making significant progress in research. In addition, existing studies have applied main biological evolutionary models to artifacts although such methods do not fit to them. These models are also limited to generalize evolutionary patterns of artifacts because they are designed in terms of a subjective point of view of experts who know well about the artifacts. Unlike biological organisms, because artifacts are likely to reflect the imagination of the human will, it is known that the theory of biological evolution cannot be directly applied to artifacts. In this paper, beyond the individual's subjective, the aim of our research is to present evolutionary patterns of a given artifact based on peeping the idea of the public. For this, we propose a text mining approach that presents a systematic framework that can find out the evolutionary patterns of a given artifact and then visualize effectively. In particular, based on our proposal, we focus mainly on a case study of mobile phone that has emerged as an icon of innovation in recent years. We collect and analyze review posts on mobile phone available in the domestic market over the past decade, and discuss the detailed results about evolutionary patterns of the mobile phone. Moreover, this kind of task is a tedious work over a long period of time because a small number of experts carry out an extensive literature survey and summarize a huge number of materials to finally draw a diagram of evolutionary patterns of the mobile phone. However, in this work, to minimize the human efforts, we present a semi-automatic mining algorithm, and through this research we can understand how human creativity and imagination are implemented. In addition, it is a big help to predict the future trend of mobile phone in business and industries.

• Journal of Intelligence and Information Systems
• /
• v.21 no.3
• /
• pp.19-36
• /
• 2015

Social media is an emerging issue in content services and in current business environment. YouTube is the most representative social media service in the world. YouTube is different from other conventional content services in its open user participation and contents creation methods. To promote a content in YouTube, it is important to understand the diffusion phenomena of contents and the network structural characteristics. Most previous studies analyzed impact factors of contents diffusion from the view point of general behavioral factors. Currently some researchers use network structure factors. However, these two approaches have been used separately. However this study tries to analyze the general impact factors on the view count and content based network structures all together. In addition, when building a content based network, this study forms the network structure by analyzing user comments on 22,370 contents of YouTube not based on the individual user based network. From this study, we re-proved statistically the causal relations between view count and not only general factors but also network factors. Moreover by analyzing this integrated research model, we found that these factors affect the view count of YouTube according to the following order; Uploader Followers, Video Age, Betweenness Centrality, Comments, Closeness Centrality, Clustering Coefficient and Rating. However Degree Centrality and Eigenvector Centrality affect the view count negatively. From this research some strategic points for the utilizing of contents diffusion are as followings. First, it is needed to manage general factors such as the number of uploader followers or subscribers, the video age, the number of comments, average rating points, and etc. The impact of average rating points is not so much important as we thought before. However, it is needed to increase the number of uploader followers strategically and sustain the contents in the service as long as possible. Second, we need to pay attention to the impacts of betweenness centrality and closeness centrality among other network factors. Users seems to search the related subject or similar contents after watching a content. It is needed to shorten the distance between other popular contents in the service. Namely, this study showed that it is beneficial for increasing view counts by decreasing the number of search attempts and increasing similarity with many other contents. This is consistent with the result of the clustering coefficient impact analysis. Third, it is important to notice the negative impact of degree centrality and eigenvector centrality on the view count. If the number of connections with other contents is too much increased it means there are many similar contents and eventually it might distribute the view counts. Moreover, too high eigenvector centrality means that there are connections with popular contents around the content, and it might lose the view count because of the impact of the popular contents. It would be better to avoid connections with too powerful popular contents. From this study we analyzed the phenomenon and verified diffusion factors of Youtube contents by using an integrated model consisting of general factors and network structure factors. From the viewpoints of social contribution, this study might provide useful information to music or movie industry or other contents vendors for their effective contents services. This research provides basic schemes that can be applied strategically in online contents marketing. One of the limitations of this study is that this study formed a contents based network for the network structure analysis. It might be an indirect method to see the content network structure. We can use more various methods to establish direct content network. Further researches include more detailed researches like an analysis according to the types of contents or domains or characteristics of the contents or users, and etc.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.14 no.5
• /
• pp.25-42
• /
• 2019

The purpose of this study is to analyze the effect of internal competence of small business on the competitive advantage and start-up intention. Through this, which key competence of small business can be proposed for market growth against of large companies that have a great capital power. Also, implications for development direction can be suggested. For this purpose, technology capability, financing capability, marketing capability, and product/service differentiation capability factors were set as independent variables, as well as competitive advantage as a mediating factor, and startup intention to be franchiser as a dependent variable. For the analysis, a structured questionnaires survey was conducted to 276 domestic small business in capital area. The main results are as follows. First, in relation to the internal competency and the competitive advantage of small businesses, it was found that technological capabilities, financing capabilities and product (service) differentiation have a positive effect on competitive advantage. Second, the relationship between the internal capacity of a small entity and startup intention to commercialize a franchiser business indicates that its financing capacity and marketing capabilities have a positive effect on startup intention to commercialize the business, and that competitive advantage has a significant effect on startup intention to commercialize the franchise. Third, competitive advantage was found to mediate the relationship between internal competency and startup intention. Finally, it was shown that the internal capacity of a small business has a regulating effect in relation to its financing capacity, marketing capabilities and startup intention to commercialize the franchise, and that it also had a regulating effect in relation to its relationship with its competitive advantage and franchise suitability. Based on the above results, if small business can take competitive advantage in the market, they also consider startup intention to commercialize a franchiser, in addition, it is expected that one suggestion can be made from an internal capacity perspective required more emphasis on operations and management as an alternative to expanding small businesses' business, including market access measures that can be linked to internal capacity factors of small businesses.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.14 no.1
• /
• pp.187-198
• /
• 2019

The purpose of this study is to seek ways of utilizing TLBMC by understanding business model of social ventures that are accompanied by social and environmental as well as economic missions. In order to achieve this, business models from economic, environmental and social perspectives will be analyzed, and we seek to enhance sustainability of social venture entrepreneurs. As the analysis tool, TLBMC(Triple Layered Business Model Canvas) expands upon the business model canvas that is widely utilized and recognizes economical terms. The TLBMC is proposed by Joyce, A., & Paquin, R. L.(2016) to help achieve a holistic view with horizontal and vertical associations. The study tries to overcome limitations of previous studies and observe a variety of economic, environmental, and social perspectives that social ventures should have with the TLBMC. As a result, it has the following implications; Firstly, creating separate social, environmental and economic business model canvas helps a business to have a holistic approach. Secondly, it was found that social venture characteristics of environmental and social perspectives were applied in practice. Lastly, we have established experience data on social venture business model. This study focuses on the opinions, the meanings and the subjective views of the participants. As a result, conclusions are drawn by the researchers ' assertions and has limitations as a research on case studies. However, this study will help people who are preparing or studying social ventures to have economic, environmental, and social perspectives. Also, redefinition of the direction and value of entrepreneurs operating social ventures, such as vision and mission, will help clarify the roles and responsibility of organizations. As a fundamental step for future empirical studies, this study could be the base for social venture business model studies.

• Korean Journal of Construction Engineering and Management
• /
• v.20 no.4
• /
• pp.114-125
• /
• 2019

Off-Site Construction (OSC) is a new construction method based on factory production. OSC (Off-Site Construction) is a new construction method based on factory production. Researches such as OSC-related design and production standardization, transport methods are actively conducted in the U.S., UK and other parts of the world as this new method has an edge over existing methods in terms of productivity, economy and quality. As the emergence of this new area requires reasonable management, an analysis of the scope of construction project management is required accordingly. Therefore, this research analyzed the study trends and relationships at the CM/PM range's "Operation level" to identify areas of study, relationship between studies and deficiencies in current research. This study carried out a comprehensive literature review of the OSC (CM/PM) research by analyzing 94 papers in Operation level as of September 3, 2018, and the analysis results are as follows. (1) Working stage level researches have been increasing rapidly since 2006. (2) Non-volumetric type is contributing most significantly at work stage level. In the building sector, it has been identified that problems such as residential: living, quality issues, non-residential: economic difficulties, factory: productivity problems have been addressed. (4) The Non-volumetric pre-assembly type dealt with the economic feasibility of residential and non-residential buildings, whereas the modular type was studied in regards to assembly quality. (5) From 2006, project management areas (e.g., quality, human resources, risks) have been expanded. It is expected that this research will help find new areas of research for OSC. If the analysis is carried out to the level of the industrial, corporate and project phases in the future, it is deemed that the overall research flow and area of the OSC industry can be identified.

• The Korean Journal of Archival Studies
• /
• no.22
• /
• pp.3-35
• /
• 2009

We have innovated the records management since 2004. So, We innovated the electronic records management, transparency, and accountability. From these results, we could mark a turning point to plant the democratic values in the government It is very surprising, but it is fact that there are the estrangement between the high level institutionalization and low level records cultural soil. But after starting new government, things have been going backward. We have experienced the hyper-politicized problem, shrinking governance problem, regressive personnel policies in the National Archives of Korea. 'New Innovation Model' has resulted the shrinking democratic values, and the growing the bureaucratism. At this point of change, it will be meaningful to review the future of records management. First, we should make the more archives to realize the self-control decentralization model. It means that all local governments has the duty to build the archives, and to operate it with a principle of autonomy. Second, We should start the culture movement to build the more archives, the small archives in private sector. Archives are necessary in the NGO, Universities, firms, art, media, etc. And the small archives are necessary in the various communities, which enhance the rights of minority. All these will spread the democratic values in our society. Third, right democracy system should be operated for the political neutrality, independency. This problem is not prohibited within the national archives innovation model. So, we should transfer the powers of government to local government, and we should re-innovate the National Archives Committee will have the role to make the important records management policies. In short, Unless going to forward with the more democratic values, it would go backward 'records management without democracy'.

• Journal of Intelligence and Information Systems
• /
• v.27 no.2
• /
• pp.1-15
• /
• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.

• Journal of Intelligence and Information Systems
• /
• v.28 no.2
• /
• pp.237-262
• /
• 2022

There has been a growing interest in IPOs (Initial Public Offerings) due to the profitable returns that IPO stocks can offer to investors. However, IPOs can be speculative investments that may involve substantial risk as well because shares tend to be volatile, and the supply of IPO shares is often highly limited. Therefore, it is crucially important that IPO investors are well informed of the issuing firms and the market before deciding whether to invest or not. Unlike institutional investors, individual investors are at a disadvantage since there are few opportunities for individuals to obtain information on the IPOs. In this regard, the purpose of this study is to provide individual investors with the information they may consider when making an IPO investment decision. This study presents a model that uses machine learning and text analysis to predict whether an IPO stock price would move up or down after the first 5 trading days. Our sample includes 691 Korean IPOs from June 2009 to December 2020. The input variables for the prediction are three tone variables created from IPO prospectuses and quantitative variables that are either firm-specific, issue-specific, or market-specific. The three prospectus tone variables indicate the percentage of positive, neutral, and negative sentences in a prospectus, respectively. We considered only the sentences in the Risk Factors section of a prospectus for the tone analysis in this study. All sentences were classified into 'positive', 'neutral', and 'negative' via text analysis using TF-IDF (Term Frequency - Inverse Document Frequency). Measuring the tone of each sentence was conducted by machine learning instead of a lexicon-based approach due to the lack of sentiment dictionaries suitable for Korean text analysis in the context of finance. For this reason, the training set was created by randomly selecting 10% of the sentences from each prospectus, and the sentence classification task on the training set was performed after reading each sentence in person. Then, based on the training set, a Support Vector Machine model was utilized to predict the tone of sentences in the test set. Finally, the machine learning model calculated the percentages of positive, neutral, and negative sentences in each prospectus. To predict the price movement of an IPO stock, four different machine learning techniques were applied: Logistic Regression, Random Forest, Support Vector Machine, and Artificial Neural Network. According to the results, models that use quantitative variables using technical analysis and prospectus tone variables together show higher accuracy than models that use only quantitative variables. More specifically, the prediction accuracy was improved by 1.45% points in the Random Forest model, 4.34% points in the Artificial Neural Network model, and 5.07% points in the Support Vector Machine model. After testing the performance of these machine learning techniques, the Artificial Neural Network model using both quantitative variables and prospectus tone variables was the model with the highest prediction accuracy rate, which was 61.59%. The results indicate that the tone of a prospectus is a significant factor in predicting the price movement of an IPO stock. In addition, the McNemar test was used to verify the statistically significant difference between the models. The model using only quantitative variables and the model using both the quantitative variables and the prospectus tone variables were compared, and it was confirmed that the predictive performance improved significantly at a 1% significance level.

• Journal of Navigation and Port Research
• /
• v.46 no.4
• /
• pp.375-384
• /
• 2022

The shipping industry is cyclically impacted by complex variables such as various economic indicators, social events, and supply and demand. The purpose of this study was to analyze the operating profit of 13 Korean liner companies over 30 years, including the financial crisis of the late 1990s, the global financial crisis of the late 2000s, and the COVID-19 global pandemic. This study was conducted to also identify factors that impacted the profit ratio of Korea's liner shipping companies according to economic conditions. It was divided into ocean-going and short-sea shipping, reflecting the characteristics of liner shipping companies, and was analyzed by hierarchical multiple regression analysis. The time series data are based on the Korean International Financial Reporting Standards (K-IFRS) and comprise seaborne trade volume, fleet evolution, and macroeconomic indicators. The outliers representing the economic downturn due to social events were separately analyzed. As a result of the analysis, the China Container Freight Index (CCFI) positively impacted ocean-going as well as short-sea liner shipping companies. However, the Korean container shipping volume only impacted ocean-going liners positively. Additionally, world and Korea's GDP, world seaborne trade volume, and fuel price are factored in the operating profit of short sea liner shipping. Also, the GDP growth rate of China, exchange rate, and interest rate did not significantly impact both groups. Notably, the operating profitability of Korea's liner shipping shows an exceptionally high rate during the recessions of 1998 and 2020. It is paradoxical, and not correlated with the classical economic indicators. Unlike other studies, this paper focused on the operating profit before financial expenses, considering the complexity as well as difficulty in forecasting the shipping cycle, and rendered conclusions using relatively long-term empirical analysis, including three economic shocks.

• Journal of Wetlands Research
• /
• v.24 no.3
• /
• pp.204-212
• /
• 2022

The LID technique began to be applied in Korea after 2009, and LID facilities are installed and operated for rainwater management in business districts such as the Ministry of Environment, the Ministry of Land, Infrastructure and Transport, and LH Corporation, public institutions, commercial land, housing, parks, and schools. However, looking at domestic cases, the application cases and operation periods are insufficient compared to those outside the country, so appropriate design standards and measures for operation and maintenance are insufficient. In particular, LID facilities constructed using LID techniques need to maintain the environment inside LID facilities because hydrological and environmental effects are expressed by material circulation and energy flow. The LID facility is designed with the treatment capacity planned for the water circulation target, and the proper maintenance, vegetation, and soil conditions are periodically identified, and the efficiency is maintained as much as possible. In other words, the soil created in LID is a very important design element because LID facilities are expected to have effects such as water pollution reduction, flood reduction, water resource acquisition, and temperature reduction while increasing water storage and penetration capacity through water circulation construction. In order to maintain and manage the functions of LID facilities accurately, the current state of the facilities and the cycle of replacement and maintenance should be accurately known through various quantitative data such as soil contamination, snow removal effects, and vegetation criteria. This study was conducted to investigate the current status of LID facilities installed in Korea from 2009 to 2020, and analyze soil changes through the continuity and current status of LID facilities applied over the past 10 years after collecting soil samples from the soil layer. Through analysis of Saturn, organic matter, hardness, water contents, pH, electrical conductivity, and salt, some vegetation-type LID facilities more than 5 to 7 years after construction showed results corresponding to the lower grade of landscape design. Facilities below the lower level can be recognized as a point of time when maintenance is necessary in a state that may cause problems in soil permeability and vegetation growth. Accordingly, it was found that LID facilities should be managed through soil replacement and replacement.