• Journal of Venture Innovation
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• v.7 no.1
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• pp.167-182
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• 2024

The aim of this study is to empirically explore the operational spillover effect among companies within chaebol groups, prominent corporate conglomerates in South Korea. Chaebols are known for their horizontal and vertical integration, fostering close collaboration among their constituent companies from a supply chain standpoint. Existing literature highlights the sharing of tangible and intangible resources within chaebol structures, leading to increased efficiency by minimizing transaction costs through resource sharing. This research investigates whether operational management performance within chaebol structures can be transmitted through cooperative resource utilization. To achieve this objective, we categorize leading companies and affiliate companies within chaebols and examine whether the operational management performance of leading companies significantly influences that of affiliate companies. Data on conglomerates, as defined by the Korea Fair Trade Commission, were collected, along with information on companies within these groups. Subsequently, the company with the highest revenue within each group was identified as the leading company, while the remaining companies were designated as affiliate companies. Our analysis reveals a significant positive relationship between the performance of inventory and facility resource management of leading companies and that of affiliate companies. This study sheds light on the transfer of operational management performance within conglomerates from a managerial perspective, underscoring the importance of reinforcing cooperation systems within the chaebol group. Furthermore, this research contributes to the academic discourse by delineating conglomerates from an operational management perspective and empirically demonstrating the transfer effect of operational management performance.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.219-239
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• 2019

As the importance of providing customized services to individuals becomes important, researches on personalized recommendation systems are constantly being carried out. Collaborative filtering is one of the most popular systems in academia and industry. However, there exists limitation in a sense that recommendations were mostly based on quantitative information such as users' ratings, which made the accuracy be lowered. To solve these problems, many studies have been actively attempted to improve the performance of the recommendation system by using other information besides the quantitative information. Good examples are the usages of the sentiment analysis on customer review text data. Nevertheless, the existing research has not directly combined the results of the sentiment analysis and quantitative rating scores in the recommendation system. Therefore, this study aims to reflect the sentiments shown in the reviews into the rating scores. In other words, we propose a new algorithm that can directly convert the user 's own review into the empirically quantitative information and reflect it directly to the recommendation system. To do this, we needed to quantify users' reviews, which were originally qualitative information. In this study, sentiment score was calculated through sentiment analysis technique of text mining. The data was targeted for movie review. Based on the data, a domain specific sentiment dictionary is constructed for the movie reviews. Regression analysis was used as a method to construct sentiment dictionary. Each positive / negative dictionary was constructed using Lasso regression, Ridge regression, and ElasticNet methods. Based on this constructed sentiment dictionary, the accuracy was verified through confusion matrix. The accuracy of the Lasso based dictionary was 70%, the accuracy of the Ridge based dictionary was 79%, and that of the ElasticNet (${\alpha}=0.3$) was 83%. Therefore, in this study, the sentiment score of the review is calculated based on the dictionary of the ElasticNet method. It was combined with a rating to create a new rating. In this paper, we show that the collaborative filtering that reflects sentiment scores of user review is superior to the traditional method that only considers the existing rating. In order to show that the proposed algorithm is based on memory-based user collaboration filtering, item-based collaborative filtering and model based matrix factorization SVD, and SVD ++. Based on the above algorithm, the mean absolute error (MAE) and the root mean square error (RMSE) are calculated to evaluate the recommendation system with a score that combines sentiment scores with a system that only considers scores. When the evaluation index was MAE, it was improved by 0.059 for UBCF, 0.0862 for IBCF, 0.1012 for SVD and 0.188 for SVD ++. When the evaluation index is RMSE, UBCF is 0.0431, IBCF is 0.0882, SVD is 0.1103, and SVD ++ is 0.1756. As a result, it can be seen that the prediction performance of the evaluation point reflecting the sentiment score proposed in this paper is superior to that of the conventional evaluation method. In other words, in this paper, it is confirmed that the collaborative filtering that reflects the sentiment score of the user review shows superior accuracy as compared with the conventional type of collaborative filtering that only considers the quantitative score. We then attempted paired t-test validation to ensure that the proposed model was a better approach and concluded that the proposed model is better. In this study, to overcome limitations of previous researches that judge user's sentiment only by quantitative rating score, the review was numerically calculated and a user's opinion was more refined and considered into the recommendation system to improve the accuracy. The findings of this study have managerial implications to recommendation system developers who need to consider both quantitative information and qualitative information it is expect. The way of constructing the combined system in this paper might be directly used by the developers.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.67-88
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• 2018

Until recently, as we recognize the significance of sustainable growth and competitiveness of small-and-medium sized enterprises (SMEs), governmental support for tangible resources such as R&D, manpower, funds, etc. has been mainly provided. However, it is also true that the inefficiency of support systems such as underestimated or redundant support has been raised because there exist conflicting policies in terms of appropriateness, effectiveness and efficiency of business support. From the perspective of the government or a company, we believe that due to limited resources of SMEs technology development and capacity enhancement through collaboration with external sources is the basis for creating competitive advantage for companies, and also emphasize value creation activities for it. This is why value chain network analysis is necessary in order to analyze inter-company deal relationships from a series of value chains and visualize results through establishing knowledge ecosystems at the corporate level. There exist Technology Opportunity Discovery (TOD) system that provides information on relevant products or technology status of companies with patents through retrievals over patent, product, or company name, CRETOP and KISLINE which both allow to view company (financial) information and credit information, but there exists no online system that provides a list of similar (competitive) companies based on the analysis of value chain network or information on potential clients or demanders that can have business deals in future. Therefore, we focus on the "Value Chain Network System (VCNS)", a support partner for planning the corporate business strategy developed and managed by KISTI, and investigate the types of embedded network-based analysis modules, databases (D/Bs) to support them, and how to utilize the system efficiently. Further we explore the function of network visualization in intelligent value chain analysis system which becomes the core information to understand industrial structure ystem and to develop a company's new product development. In order for a company to have the competitive superiority over other companies, it is necessary to identify who are the competitors with patents or products currently being produced, and searching for similar companies or competitors by each type of industry is the key to securing competitiveness in the commercialization of the target company. In addition, transaction information, which becomes business activity between companies, plays an important role in providing information regarding potential customers when both parties enter similar fields together. Identifying a competitor at the enterprise or industry level by using a network map based on such inter-company sales information can be implemented as a core module of value chain analysis. The Value Chain Network System (VCNS) combines the concepts of value chain and industrial structure analysis with corporate information simply collected to date, so that it can grasp not only the market competition situation of individual companies but also the value chain relationship of a specific industry. Especially, it can be useful as an information analysis tool at the corporate level such as identification of industry structure, identification of competitor trends, analysis of competitors, locating suppliers (sellers) and demanders (buyers), industry trends by item, finding promising items, finding new entrants, finding core companies and items by value chain, and recognizing the patents with corresponding companies, etc. In addition, based on the objectivity and reliability of the analysis results from transaction deals information and financial data, it is expected that value chain network system will be utilized for various purposes such as information support for business evaluation, R&D decision support and mid-term or short-term demand forecasting, in particular to more than 15,000 member companies in Korea, employees in R&D service sectors government-funded research institutes and public organizations. In order to strengthen business competitiveness of companies, technology, patent and market information have been provided so far mainly by government agencies and private research-and-development service companies. This service has been presented in frames of patent analysis (mainly for rating, quantitative analysis) or market analysis (for market prediction and demand forecasting based on market reports). However, there was a limitation to solving the lack of information, which is one of the difficulties that firms in Korea often face in the stage of commercialization. In particular, it is much more difficult to obtain information about competitors and potential candidates. In this study, the real-time value chain analysis and visualization service module based on the proposed network map and the data in hands is compared with the expected market share, estimated sales volume, contact information (which implies potential suppliers for raw material / parts, and potential demanders for complete products / modules). In future research, we intend to carry out the in-depth research for further investigating the indices of competitive factors through participation of research subjects and newly developing competitive indices for competitors or substitute items, and to additively promoting with data mining techniques and algorithms for improving the performance of VCNS.

• Cartoon and Animation Studies
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• s.34
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• pp.317-344
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• 2014

Perspectives on the basis of arts and cultural management, this study intends to suggest improvements in core curriculums that are required in order for South Korea, a country that has initiated into the animation industry through outsourcing from big-budget animation production countries such as America and Japan, to develop its own strong base in creative animation industry. The perspectives of arts management in this context means an integration nexus between human studies, social science and management, and suggestions are as follow: First, it is crucial to understand the current trend of animation industry structure across the globe, as well as to develop the ability of co-production. Animation industry often requires technical skills, capital strength and human resources, each having equal importance. Therefore, thorough analysis of the three components in worldwide animation industry must be preceded for animation production services. To do so, collaboration with major animation creation countries is the best option and is highly encouraged, so that the national animation curriculum shall be enhanced to meet such demands and hence develop various abilities. The second is a good understanding of new-media and new-platforms. Not only the traditional distributor of animation such as television and theater, the distribution system expands its scope to a variety of online sources including pod-casts and the Internet. Under these circumstances, a deep understanding towards animation distribution system and an analysis of the new consumer channel are also of paramount importance for animation production. Third, a possibility of animation supply chain through diversified routes and media have paved the way for a possible animation production services and distribution without a mega-budget. Thus, new curriculum shall need to reinforce marketing and management aspects that will in turn help individuals to establish a self-employed creative business. Last but not least, this study further includes illustration of current curriculum of animation studies in national universities, followed by detailed suggestions for the curriculum improvements based on the above mentioned three factors. It was observed that the current curriculums have been solely focused on practical works and technical skills of animation and art studies; a four-year-course colleges that provide animation courses usually lack components of human studies, social science and management. Thus, this study proposes essential contexts of management studies that are needed for individual business and also curriculum improvements that are derived from the analysis of the current industry and the new media.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.227-240
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• 2011

New concepts and ideas often result from extensive recombination of existing concepts or ideas. Both researchers and developers build on existing concepts and ideas in published papers or registered patents to develop new theories and technologies that in turn serve as a basis for further development. As the importance of patent increases, so does that of patent analysis. Patent analysis is largely divided into network-based and keyword-based analyses. The former lacks its ability to analyze information technology in details while the letter is unable to identify the relationship between such technologies. In order to overcome the limitations of network-based and keyword-based analyses, this study, which blends those two methods, suggests the keyword network based analysis methodology. In this study, we collected significant technology information in each patent that is related to Light Emitting Diode (LED) through text mining, built a keyword network, and then executed a community network analysis on the collected data. The results of analysis are as the following. First, the patent keyword network indicated very low density and exceptionally high clustering coefficient. Technically, density is obtained by dividing the number of ties in a network by the number of all possible ties. The value ranges between 0 and 1, with higher values indicating denser networks and lower values indicating sparser networks. In real-world networks, the density varies depending on the size of a network; increasing the size of a network generally leads to a decrease in the density. The clustering coefficient is a network-level measure that illustrates the tendency of nodes to cluster in densely interconnected modules. This measure is to show the small-world property in which a network can be highly clustered even though it has a small average distance between nodes in spite of the large number of nodes. Therefore, high density in patent keyword network means that nodes in the patent keyword network are connected sporadically, and high clustering coefficient shows that nodes in the network are closely connected one another. Second, the cumulative degree distribution of the patent keyword network, as any other knowledge network like citation network or collaboration network, followed a clear power-law distribution. A well-known mechanism of this pattern is the preferential attachment mechanism, whereby a node with more links is likely to attain further new links in the evolution of the corresponding network. Unlike general normal distributions, the power-law distribution does not have a representative scale. This means that one cannot pick a representative or an average because there is always a considerable probability of finding much larger values. Networks with power-law distributions are therefore often referred to as scale-free networks. The presence of heavy-tailed scale-free distribution represents the fundamental signature of an emergent collective behavior of the actors who contribute to forming the network. In our context, the more frequently a patent keyword is used, the more often it is selected by researchers and is associated with other keywords or concepts to constitute and convey new patents or technologies. The evidence of power-law distribution implies that the preferential attachment mechanism suggests the origin of heavy-tailed distributions in a wide range of growing patent keyword network. Third, we found that among keywords that flew into a particular field, the vast majority of keywords with new links join existing keywords in the associated community in forming the concept of a new patent. This finding resulted in the same outcomes for both the short-term period (4-year) and long-term period (10-year) analyses. Furthermore, using the keyword combination information that was derived from the methodology suggested by our study enables one to forecast which concepts combine to form a new patent dimension and refer to those concepts when developing a new patent.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.1
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• pp.1-12
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• 2015

The purpose of this study is to examine the effect of company characteristics and individual characteristics perceived by employees of small businesses on job satisfaction and especially to seek activation of the organization by extending from previous studies to examine the intermediary role of company innovation and applying management measures focusing on the environment of changing global society where CEOs of small businesses cause job satisfaction of organizational members and present the direction for the improvement of management and institutional development. This study was carried out for about 2 months targeting employees of small businesses and the results of empirical analysis are as follows: First, company characteristics and individual characteristics perceived by employees of small businesses turned out to have a positive (+)effect on job satisfaction but the hypothesis that job stress affects job satisfaction was not significant. Second, of the effects of company characteristics and individual characteristics perceived by employees of small businesses on company innovation, organization flexibility and CEO's leadership, company communication and degree of cooperation between departments, individuals, challenge of individuals perceived individual characteristics were found to affect company innovation but the hypothesis that job stress affects it was not significant. Third, company innovation was found to have a positive (+)effect on job satisfaction and fourth, in the intermediary effect verification of company innovation between company characteristics and individual characteristics perceived by employees and job satisfaction, organization flexibility and communication, collaboration turned out to perform partial intermediation and CEO's leadership to perform full intermediation and individual challenge performance to perform full intermediation and the intermediary effect of job stress was not proven. These results are company characteristics and individual characteristics that is the perception of the independent variables in SME employees is not only a direct relationship with job satisfaction, suggesting that also has an indirect effect is mediated depending on the innovation of the company. Therefore, it can be seen that even for the innovation performance of enterprises is important to increase the job satisfaction of employees of SMEs. In particular, the conductivity of the leadership and individual parameters so completely over the innovativeness of the company is the result of job satisfaction itgetda can be said that the innovation efforts of the organization is effective at the same time be pursued.

• Asia pacific journal of information systems
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• v.23 no.3
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• pp.1-23
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• 2013

Open Source Software Development (OSSD) differentiates itself from traditional closed software development in that it reveals its source codes online and allows anyone to participate in projects. Even though its success was in doubt, many of the open collaborative working models produced successful results. Academia started to get interested in how developers are willing to participate even when there are no extrinsic rewards for their efforts. Many studies tried to explain developers' motivations, and the pursuit of ideology, reputation, and altruism are found to be the answers. Those studies, however, focused mostly on how the first contribution is made out of a certain motivation. Nowadays, OSSD reaches at its maturity and 70% of professional developers have used or utilized open source software or code in their works. As the proportion of people experiencing OSS, the accounts from previous studies are expected to be weakened. Also, extant literature fails to explain how the motivation of participating in OSS evolves over time and experiences. Given that changing over time or over experiences is the natural in the perception of motivation, studies in an attempt to understand how the motivation changes or evolves are in need. In this study, we aimed to explain how the perception about OSS from past usage or related experiences leads to the intention to sustain OSS participations. By doing so, we try to bridge the gap between previous studies and the actual phenomenon. We argued that perceived instrumentality about OSS learned from past experiences will first affect the formation of organizational identity towards general OSS community. And once the organizational identity is formed, it will affect the one's following behaviors related to OSS development, most likely to sustain the favoring stance toward OSS community. Our research distinguishes itself from previous one in that it divides the paths from organizational identity formed to the intention to sustain the voluntary helping behaviors, by altruistic and conforming intentions. Drawing on this structural model, we could explain how organizational identity engages in forming the sustaining intention from past experiences, and that the intention to help at individual level and organizational level works at different level in OSS community. We grounded our arguments on Social identity theory and Organizational Citizenship theory. We examined our assumption by constructing a structural equation model (SEM) and had 88 developers to answer our online surveys. The result is analyzed by PLS (partial least square) method. Consequently, all paths but one in our model are supported, the one which assumed the association between perceived instrumentality and altruistic intention. Our results provide directions in designing online collaborative platforms where open access collaboration is meant to occur. Theoretically, our study suggests that organizational citizenship behavior can occur from organizational identity, even in bottom-up organizational settings. More specifically, we also argue to consider both organizational level and individual level of motivation in inducing sustained participations within the platforms. Our result can be interpreted to indicate the importance of forming organizational identity in sustaining the participatory behaviors. It is because there was no direct association between perceived instrumentality from past experiences and altruistic behavior, but the perception of organizational identity bridges the two constructs. This means that people with no organizational identity can sustain their participations through conforming intention from only the perception of instrumentality, but it needs little more than that for the people to feel the intention to directly help someone in the community-first to form the self-identity as a member of the given community.

• The Korean Journal of Archival Studies
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• no.18
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• pp.139-178
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• 2008

The functional requirements standards for electronic records management systems which have been published recently describe the specifications very precisely including not only core functions of records management but also the function of system management and optional modules. The fact that these functional requirements standards seem to be similar to each other in terms of the content of functions described in the standards is linked to the global standardization trends in the practical area of electronic records. In addition, these functional requirements standards which have been built upon with collaboration of archivists from many national archives, IT specialists, consultants and records management applications vendors result in not only obtaining high quality but also establishing the condition that the standards could be the certificate criteria easily. Though there might be a lot of different ways and approaches to benchmark the functional requirements standards developed from advanced electronic records management practice, this paper is showing the possibility and meaningful business cases of gaining useful practical ideas learned from imaging electronic records management practices related to the functional requirements standards. The business cases are explored central functions of records management and the intellectual control of the records such as classification scheme or disposal schedules. The first example is related to the classification scheme. Should the records classification be fixed at same number of level? Should a record item be filed only at the last node of classification scheme? The second example addresses a precise disposition schedule which is able to impose the event-driven chronological retention period to records and which could be operated using a inheritance concept between the parent nodes and child nodes in classification scheme. The third example shows the usage of the function which holds or freeze and release the records required to keep as evidence to comply with compliance like e-Discovery or the risk management of organizations under the premise that the records management should be the basis for the legal compliance. The last case shows some cases for bulk batch operation required if the records manager can use the ERMS as their useful tool. It is needed that the records managers are able to understand and interpret the specifications of functional requirements standards for ERMS in the practical view point, and to review the standards and extract required specifications for upgrading their own ERMS. The National Archives of Korea should provide various stakeholders with a sound basis for them to implement effective and efficient electronic records management practices through expanding the usage scope of the functional requirements standard for ERMS and making the common understanding about its implications.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.6
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• pp.67-79
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• 2020

This study analyzed the start-up education curriculum and start-up education programs of seven universities in the U.S. to find out what courses are provided, what various programs exist, and what the characteristics of start-up education in each university are. California State University, San Bernardino / University of California, Irvine / Drexel University / Oklahoma State University / Florida State University / San Diego State University / University of Southern California where entrepreneurship education based on the Entrepreneurship Degree Course is being established based on the Entrepreneurship Center of seven universities in the United States, which is not well introduced in Korea. This study examined how the start-up education courses and start-up support systems at seven universities in the U.S. are progressing at the undergraduate, MBA, master's and doctoral levels, and comparative levels. Through the case studies of the universities presented, the primary analysis was carried out to explore the various characteristics of American university start-up education. The implications of start-up education at American universities in this study are as follows. First, in order for universities to take the initiative in providing start-up education, they should be organized to suit the course of start-up education suitable for the characteristics of universities and introduce support programs. Second, it is necessary to establish an independent center within domestic universities to be operated autonomously. Third, the start-up education of universities should include building university-industry partnerships, operating entrepreneurship degree courses and collaboration between departments of universities. Fourth, the independent center should lead the active participation of alumni and local start-ups and start-up-related programs should be operated based on this. Fifth, Differentiated programs for each university's characteristics should be introduced and applied to universities. Although case studies have limitations that cannot be generalized, they can provide a useful framework. Therefore, it is necessary to design a systematic start-up education that reflects the correct design direction and characteristics of each university.