• Korean Security Journal
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• no.39
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• pp.185-215
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• 2014

Since North Korea has used terror crime as a means of unification under communism against South Korea, South Korea has been much damaged until now. And the occurrence possibility of terror crime by North Korean authority is now higher than any other time. The North Korean terror crimes of Kim Il Sung era had been committed by the dictator's instruction with the object of securing governing fund. However, looking at the terror crimes committed for decades during Kim Jung Il authority, it is revealed that these terror crimes are expressed as a criminal behavior because of the conflict to accomplish the power and economic advantage non powerful groups target. This study focused on the power conflict in various causes of terror crimes by applying George B. Vold(1958)'s theory which explained power conflict between groups became a factor of crime, and found the aspect by ages of terror crime behavior by North Korean authority and responding plan to future North Korean terror crime. North Korean authority high-ranking officials were the Labor Party focusing on Juche Idea for decades in Kim Il Sung time. Afterwards, high-ranking officials were formed focusing on military authorities following Military First Policy at the beginning of Kim Jung Il authority, rapid power change has been done for recent 10 years. To arrange the aspect by times of terror crime following this power change, alienated party executives following the support of positive military first authority by Kim Jung Il after 1995 could not object to forcible terror crime behavior of military authority, and 1st, 2nd Yeongpyeong maritime war which happened this time was propelled by military first authority to show the power of military authority. After 2006, conservative party union enforced censorship and inspection on the trade business and foreign currency-earning of military authority while executing drastic purge. The shooting on Keumkangsan tourists that happened this time was a forcible terror crime by military authority following the pressure of conservative party. After October, 2008, first military reign union executed the launch of Gwanmyungsung No.2 long-range missile, second nuclear test, Daechung marine war, and Cheonanham attacking terror in order to highlight the importance and role of military authority. After September 2010, new reign union went through severe competition between new military authority and new mainstream and new military authority at this time executed highly professionalized terror crime such as cyber/electronic terror unlike past military authority. After July 2012, ICBM test launch, third nuclear test, cyber terror on Cheongwadae homepage of new mainstream association was the intention of Km Jung Eun to display his ability and check and adjust the power of party/military/cabinet/ public security organ, and he can attempt the unexpected terror crime in the future. North Korean terror crime has continued since 1980s when Kim Jung Il's power succession was carried out, and the power aspect by times has rapidly changed since 1994 when Kim Il Sung died and the terror crime became intense following the power combat between high-ranking officials and power conflict for right robbery. Now South Korea should install the specialized department which synthesizes and analyzes the information on North Korean high-ranking officials and reinforce the comprehensive information-collecting system through the protection and management of North Korean defectors and secret agents in order to determine the cause of North Korean terror crime and respond to it. And South Korea should participate positively in the international collaboration related to North Korean terror and make direct efforts to attract the international agreement to build the international cooperation for the response to North Korean terror crime. Also, we should try more to arrange the realistic countermeasure against North Korean cyber/electronic terror which was more diversified with the expertise terror escaping from existing forcible terror through enactment/revision of law related to cyber terror crime, organizing relevant institute and budget, training professional manpower, and technical development.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.109-131
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• 2014

As the demand of nuclear power plant equipment is continuously growing worldwide, the importance of handling nuclear strategic materials is also increasing. While the number of cases submitted for the exports of nuclear-power commodity and technology is dramatically increasing, preadjudication (or prescreening to be simple) of strategic materials has been done so far by experts of a long-time experience and extensive field knowledge. However, there is severe shortage of experts in this domain, not to mention that it takes a long time to develop an expert. Because human experts must manually evaluate all the documents submitted for export permission, the current practice of nuclear material export is neither time-efficient nor cost-effective. Toward alleviating the problem of relying on costly human experts only, our research proposes a new system designed to help field experts make their decisions more effectively and efficiently. The proposed system is built upon case-based reasoning, which in essence extracts key features from the existing cases, compares the features with the features of a new case, and derives a solution for the new case by referencing similar cases and their solutions. Our research proposes a framework of case-based reasoning system, designs a case-based reasoning system for the control of nuclear material exports, and evaluates the performance of alternative keyword extraction methods (full automatic, full manual, and semi-automatic). A keyword extraction method is an essential component of the case-based reasoning system as it is used to extract key features of the cases. The full automatic method was conducted using TF-IDF, which is a widely used de facto standard method for representative keyword extraction in text mining. TF (Term Frequency) is based on the frequency count of the term within a document, showing how important the term is within a document while IDF (Inverted Document Frequency) is based on the infrequency of the term within a document set, showing how uniquely the term represents the document. The results show that the semi-automatic approach, which is based on the collaboration of machine and human, is the most effective solution regardless of whether the human is a field expert or a student who majors in nuclear engineering. Moreover, we propose a new approach of computing nuclear document similarity along with a new framework of document analysis. The proposed algorithm of nuclear document similarity considers both document-to-document similarity (${\alpha}$) and document-to-nuclear system similarity (${\beta}$), in order to derive the final score (${\gamma}$) for the decision of whether the presented case is of strategic material or not. The final score (${\gamma}$) represents a document similarity between the past cases and the new case. The score is induced by not only exploiting conventional TF-IDF, but utilizing a nuclear system similarity score, which takes the context of nuclear system domain into account. Finally, the system retrieves top-3 documents stored in the case base that are considered as the most similar cases with regard to the new case, and provides them with the degree of credibility. With this final score and the credibility score, it becomes easier for a user to see which documents in the case base are more worthy of looking up so that the user can make a proper decision with relatively lower cost. The evaluation of the system has been conducted by developing a prototype and testing with field data. The system workflows and outcomes have been verified by the field experts. This research is expected to contribute the growth of knowledge service industry by proposing a new system that can effectively reduce the burden of relying on costly human experts for the export control of nuclear materials and that can be considered as a meaningful example of knowledge service application.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.127-147
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• 2020

The world is suffering from numerous human and economic losses due to the novel coronavirus infection (COVID-19). The Korean government established a strategy to overcome the national infectious disease crisis through research and development. It is difficult to find distinctive features and changes in a specific R&D field when using the existing technical classification or science and technology standard classification. Recently, a few studies have been conducted to establish a classification system to provide information about the investment research areas of infectious diseases in Korea through a comparative analysis of Korea government-funded research projects. However, these studies did not provide the necessary information for establishing cooperative research strategies among countries in the infectious diseases, which is required as an execution plan to achieve the goals of national health security and fostering new growth industries. Therefore, it is inevitable to study information services based on the classification system and classification model for establishing a national collaborative R&D strategy. Seven classification - Diagnosis_biomarker, Drug_discovery, Epidemiology, Evaluation_validation, Mechanism_signaling pathway, Prediction, and Vaccine_therapeutic antibody - systems were derived through reviewing infectious diseases-related national-funded research projects of South Korea. A classification system model was trained by combining Scopus data with a bidirectional RNN model. The classification performance of the final model secured robustness with an accuracy of over 90%. In order to conduct the empirical study, an infectious disease classification system was applied to the coronavirus-related research and development projects of major countries such as the STAR Metrics (National Institutes of Health) and NSF (National Science Foundation) of the United States(US), the CORDIS (Community Research & Development Information Service)of the European Union(EU), and the KAKEN (Database of Grants-in-Aid for Scientific Research) of Japan. It can be seen that the research and development trends of infectious diseases (coronavirus) in major countries are mostly concentrated in the prediction that deals with predicting success for clinical trials at the new drug development stage or predicting toxicity that causes side effects. The intriguing result is that for all of these nations, the portion of national investment in the vaccine_therapeutic antibody, which is recognized as an area of research and development aimed at the development of vaccines and treatments, was also very small (5.1%). It indirectly explained the reason of the poor development of vaccines and treatments. Based on the result of examining the investment status of coronavirus-related research projects through comparative analysis by country, it was found that the US and Japan are relatively evenly investing in all infectious diseases-related research areas, while Europe has relatively large investments in specific research areas such as diagnosis_biomarker. Moreover, the information on major coronavirus-related research organizations in major countries was provided by the classification system, thereby allowing establishing an international collaborative R&D projects.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.149-161
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• 2014

The export of domestic public services to overseas markets contains many potential obstacles, stemming from different export procedures, the target services, and socio-economic environments. In order to alleviate these problems, the business incubation platform as an open business ecosystem can be a powerful instrument to support the decisions taken by participants and stakeholders. In this paper, we propose an ontology model and its implementation processes for the business incubation platform with an open and pervasive architecture to support public service exports. For the conceptual model of platform ontology, export case studies are used for requirements analysis. The conceptual model shows the basic structure, with vocabulary and its meaning, the relationship between ontologies, and key attributes. For the implementation and test of the ontology model, the logical structure is edited using Prot$\acute{e}$g$\acute{e}$ editor. The core engine of the business incubation platform is the simulator module, where the various contexts of export businesses should be captured, defined, and shared with other modules through ontologies. It is well-known that an ontology, with which concepts and their relationships are represented using a shared vocabulary, is an efficient and effective tool for organizing meta-information to develop structural frameworks in a particular domain. The proposed model consists of five ontologies derived from a requirements survey of major stakeholders and their operational scenarios: service, requirements, environment, enterprise, and county. The service ontology contains several components that can find and categorize public services through a case analysis of the public service export. Key attributes of the service ontology are composed of categories including objective, requirements, activity, and service. The objective category, which has sub-attributes including operational body (organization) and user, acts as a reference to search and classify public services. The requirements category relates to the functional needs at a particular phase of system (service) design or operation. Sub-attributes of requirements are user, application, platform, architecture, and social overhead. The activity category represents business processes during the operation and maintenance phase. The activity category also has sub-attributes including facility, software, and project unit. The service category, with sub-attributes such as target, time, and place, acts as a reference to sort and classify the public services. The requirements ontology is derived from the basic and common components of public services and target countries. The key attributes of the requirements ontology are business, technology, and constraints. Business requirements represent the needs of processes and activities for public service export; technology represents the technological requirements for the operation of public services; and constraints represent the business law, regulations, or cultural characteristics of the target country. The environment ontology is derived from case studies of target countries for public service operation. Key attributes of the environment ontology are user, requirements, and activity. A user includes stakeholders in public services, from citizens to operators and managers; the requirements attribute represents the managerial and physical needs during operation; the activity attribute represents business processes in detail. The enterprise ontology is introduced from a previous study, and its attributes are activity, organization, strategy, marketing, and time. The country ontology is derived from the demographic and geopolitical analysis of the target country, and its key attributes are economy, social infrastructure, law, regulation, customs, population, location, and development strategies. The priority list for target services for a certain country and/or the priority list for target countries for a certain public services are generated by a matching algorithm. These lists are used as input seeds to simulate the consortium partners, and government's policies and programs. In the simulation, the environmental differences between Korea and the target country can be customized through a gap analysis and work-flow optimization process. When the process gap between Korea and the target country is too large for a single corporation to cover, a consortium is considered an alternative choice, and various alternatives are derived from the capability index of enterprises. For financial packages, a mix of various foreign aid funds can be simulated during this stage. It is expected that the proposed ontology model and the business incubation platform can be used by various participants in the public service export market. It could be especially beneficial to small and medium businesses that have relatively fewer resources and experience with public service export. We also expect that the open and pervasive service architecture in a digital business ecosystem will help stakeholders find new opportunities through information sharing and collaboration on business processes.

• 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.

• MISULJARYO - National Museum of Korea Art Journal
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• v.100
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• pp.140-170
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• 2021

This paper investigates the Wooden Seated Tri-kaya Buddha Images(三身佛像) of Vairocana, Rushana, and Sakyamuni enshrined in Daeungjeon Hall of Hwaeomsa temple(華嚴寺) in Gurae, South Cheolla Province. They were produced in 1634 CE and placed in 1635 CE, about forty years after original images made in the Goryeo period were destroyed by the Japanese army during the war. The reconstruction of Hwaeomsa was conducted by Gakseong, one of the leading monks of Joseon Dynasty in the 17th century, who also conducted the reconstructions of many Buddhist temples after the war. In 2015, a prayer text (dated 1635) concerning the production of Hwaeomsa Tri-kaya Buddha images was found in the repository within Sakyamuni Buddha. It lists the names of participants, including royal family members (i.e., prince Yi Guang, the eighth son of King Seon-jo), and their relatives (i.e., Sin Ik-seong, son-in-law of King Seonjo), court ladies, monk-sculptors, and large numbers of monks and laymen Buddhists. A prayer text (dated 1634) listing the names of monk-sculptors written on the wooden panel inside the pedestal of Rushana Buddha was also found. A recent investigation into the repository within Rushana Buddha in 2020 CE has revealed a prayer text listing participants producing these images, similar to the former one from Sakyamuni Buddha, together with sacred relics of hoo-ryeong-tong copper bottle and a large quantity of Sutra books. These new materials opened a way to understand Hwaeomsa Trikaya images, including who made them and when they were made. The two above-mentioned prayer texts from the repository of Sakyamuni and Rushana Buddha statues, and the wooden panel inside the pedestal of Rushan Buddha tell us that eighteen monk-sculptors, including Eungwon, Cheongheon and Ingyun, who were well-known monk artisans of the 17th century, took part in the construction of these images. As a matter of fact, Cheongheon belonged to a different workshop from Eungwon and Ingyun, who were most likely teacher and disciple or senior and junior colleagues, which means that the production of Hwaeomsa Tri-kaya Buddha images was a collaboration between sculptors from two workshops. Eungwon and Ingyun seem to have belonged to the same community studying under the great Buddhist priest Seonsu, the teacher of Monk Gakseong who was in charge of the reconstruction of Haweonsa temple. Hwaeomsa Tri-kaya Buddha images show a big head, a squarish face with plump cheeks, narrow and drooping shoulders, and a short waist, which depict significant differences in body proportion to those of other Buddha statues of the first half of 17th century, which typically have wide shoulders and long waists. The body proportion shown in the Hwaeomsa images could be linked with images of late Goryeo and early Joseon period. Rushana Buddha, raising his two arms in a preaching hand gesture and wearing a crown and bracelets, shows unique iconography of the Bodhisattva form. This iconography of Rushana Buddha had appeared in a few Sutra paintings of Northern Song and Late Goryeo period of 13th and 14th century. BodhaSri-mudra of Vairocana Buddha, unlike the general type of BodhaSri-mudra that shows the right hand holding the left index finger, places his right hand upon the left hand in a fist. It is similar to that of Vairocana images of Northern and Southern Song, whose left hand is placed on the top of right hand in a fist. This type of mudra was most likely introduced during the Goryeo period. The dried lacquer Seated Vairocana image of Bulheosa Temple in Naju is datable to late Goryeo period, and exhibits similar forms of the mudra. Hwaeomsa Tri-kaya Buddha images also show new iconographic aspects, as well as traditional stylistic and iconographic features. The earth-touching (bhumisparsa) mudra of Sakymuni Buddha, putting his left thumb close to the middle finger, as if to make a preaching mudra, can be regarded as a new aspect that was influenced by the Sutra illustrations of the Ming dynasty, which were imported by the royal court of Joseon dynasty and most likely had an impact on Joseon Buddhist art from the 15th and 16th centuries. Stylistic and iconographical features of Hwaeomsa Tri-kaya Buddha images indicate that the traditional aspects of Goryeo period and new iconography of Joseon period are rendered together, side by side, in these sculptures. The coexistence of old and new aspects in one set of images could indicate that monk sculptors tried to find a new way to produce Hwaeomsa images based on the old traditional style of Goryeo period when the original Tri-kaya Buddha images were made, although some new iconography popular in Joseon period was also employed in the images. It is also probable that monk sculptors of Hwaeomsa Tri-kaya Buddha images intended to reconstruct these images following the original images of Goryeo period, which was recollected by surviving monks at Hwaeomsa, who had witnessed the original Tri-kaya Buddha images.

• Asia Marketing Journal
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• v.14 no.2
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• pp.97-125
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• 2012

Companies recently introduce art collaborated products incorporating culture into a product. Art collaborated products include incorporating famous movies and/or design of an artist into a newly launched product. The introduction of art collaborated products are gradually increasing. However, research for this trend is relatively scarce. Although research concerning design has discussed a number of different factors as playing a role in influencing responses to design including culture, fashion, innate preferences, etc.), only limited attention has been paid to the processes by which consumers generate responses to product designs. People with different characteristics may respond differently. When people encounter these art products, they may become curious, may think that these products are unique, novel and innovative. People tend to show different levels of curiosity when they encounter new and novel objects, which they have rarely seen or experienced. Curiosity is defined as a desire for acquiring new knowledge and new sensory experience. Previous studies demonstrated that curiosity motivates individuals to engage in exploratory behaviors. People also show different levels of need for uniqueness, which is defined as being different from others or becoming distinctive among a larger group. Individual's need for uniqueness results from signals conveyed by the material objects that individuals choose to display. Recently, researcher have developed the need for uniqueness with three distinct constructs. These three concepts include creative choice, unpopular choice, and avoidance of similarity. Creative choice is a trait tendency of an individual by expressing or differentiating himself from others through consumptions of unique products. Unpopular choice is related to an individual's tendency to consume products, which deviates from group norms. Avoidance of similarity is linked to the avoidance of consumption behavior of products that are not famous. Past research implies that people with different levels of need for uniqueness show different motivational processes. Previous research also demonstrates that different customer emotions may be derived when consumers are exposed to these art collaborated products. Research tradition has been investigated three different emotional responses such as pleasure, arousal, and dominance. Pleasure is defined as the degree to which a person feels good, joyful, happy, or satisfied in a situation. Arousal is defined as the extent to which a person feels stimulated, active, or excited. Dominance is defined as the extent that a person feels powerful vis-a-vis the environment that surrounds him/her. Previous research show that complex, speedy, and surprising stimuli may excite consumers and thus make them more pleased and engaged in their approach behavior. However, the current study identified these emotional responses as positive emotion, negative emotion, and arousal. These derived emotions may lead consumers to approach and/or avoidance behaviors. In addition, males and females tend to respond differently when they are exposed to art collaboration products. Building on this research tradition, the current study aims to investigate the inter-relationships between individual traits such as curiosity and need for uniqueness and individual's emotional responses including positive and negative emotion and arousal when people encounter various art collaborated products. Emotional responses are proposed to influence purchase intention. Additionally, previous studies show that male and females respond differently to similar stimuli. Accordingly, gender difference are proposed to moderate the links between individual traits and emotional responses. These research aims of the current study may contribute to extending our knowledge in terms of (1) which individual characteristics are related to different emotions, and (2) how these different emotional responses inter-connected to future purchase intention of arts collaborated products. In addition, (3) the different responses to these arts collaborated products by males and females will guide managers how to concoct different strategies to these segments. The questionnaire for the present study was adopted from the previous literature and validated with a pilot test. The survey was conducted in Daegu, a third largest city in South Korea, for three weeks during June and July 2011. Most respondents were in their twenties and thirties. 350 questionnaires were distributed and among them 300 were proved to be valid (valid response rate of 85.7%). Survey questionnaires from valid 300 respondents are used to test hypotheses proposed. The structural equation model (SEM) was used to validate the research model. The measurement and structural model was tested using LISREL 8.7. The measurement model test demonstrated that consistency, convergent validity, and discriminat validity of the measurement items were acceptable. The results from the structural model demonstrate that curiosity has a positive impact on positive emotion, but not on negative emotion and arousal. Need for uniqueness has three different sub-concepts such as creative choice, unpopular choice, and avoidance of similarity. The results show that creative choice has a positive effect on arousal and positive emotion, but has a negative impact on negative emotion. Unpopular choice has a positive effect on arousal, but on neither positive nor negative emotions. Avoidance of similarity has no impact on neither emotions nor arousal. The results also demonstrated that gender has a moderating influence. Males show more negative emotion to creative and unpopular choices. Implications and future research directions are discussed in conclusion.

• Journal of Venture Innovation
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• v.5 no.1
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• pp.107-127
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• 2022

Global challenges such as the corona pandemic, climate change and the war-on-tech ensure that the demand who the technologies of the future develops and monitors prominently for will be on the agenda. Development of, and applications in, agrifood, biotech, high-tech, medtech, quantum, AI and photonics are the basis of the future earning capacity of the Netherlands and contribute to solving societal challenges, close to home and worldwide. To be like the Netherlands and Europe a strategic position in the to obtain knowledge and innovation chain, and with it our autonomy in relation to from China and the United States insurance, clear choices are needed. Brainport Eindhoven: Building on Philips' knowledge base, there is create an innovative ecosystem where more than 7,000 companies in the High-tech Systems & Materials (HTSM) collaborate on new technologies, future earning potential and international value chains. Nearly 20,000 private R&D employees work in 5 regional high-end campuses and for companies such as ASML, NXP, DAF, Prodrive Technologies, Lightyear and many others. Brainport Eindhoven has a internationally leading position in the field of system engineering, semicon, micro and nanoelectronics, AI, integrated photonics and additive manufacturing. What is being developed in Brainport leads to the growth of the manufacturing industry far beyond the region thanks to chain cooperation between large companies and SMEs. South-Holland: The South Holland ecosystem includes companies as KPN, Shell, DSM and Janssen Pharmaceutical, large and innovative SMEs and leading educational and knowledge institutions that have more than Invest €3.3 billion in R&D. Bearing Cores are formed by the top campuses of Leiden and Delft, good for more than 40,000 innovative jobs, the port-industrial complex (logistics & energy), the manufacturing industry cluster on maritime and aerospace and the horticultural cluster in the Westland. South Holland trains thematically key technologies such as biotech, quantum technology and AI. Twente: The green, technological top region of Twente has a long tradition of collaboration in triple helix bandage. Technological innovations from Twente offer worldwide solutions for the large social issues. Work is in progress to key technologies such as AI, photonics, robotics and nanotechnology. New technology is applied in sectors such as medtech, the manufacturing industry, agriculture and circular value chains, such as textiles and construction. Being for Twente start-ups and SMEs of great importance to the jobs of tomorrow. Connect these companies technology from Twente with knowledge regions and OEMs, at home and abroad. Wageningen in FoodValley: Wageningen Campus is a global agri-food magnet for startups and corporates by the national accelerator StartLife and student incubator StartHub. FoodvalleyNL also connects with an ambitious 2030 programme, the versatile ecosystem regional, national and international - including through the WEF European food innovation hub. The campus offers guests and the 3,000 private R&D put in an interesting programming science, innovation and social dialogue around the challenges in agro production, food processing, biobased/circular, climate and biodiversity. The Netherlands succeeded in industrializing in logistics countries, but it is striving for sustainable growth by creating an innovative ecosystem through a regional industry-academic research model. In particular, the Brainport Cluster, centered on the high-tech industry, pursues regional innovation and is opening a new horizon for existing industry-academic models. Brainport is a state-of-the-art forward base that leads the innovation ecosystem of Dutch manufacturing. The history of ports in the Netherlands is transforming from a logistics-oriented port symbolized by Rotterdam into a "port of digital knowledge" centered on Brainport. On the basis of this, it can be seen that the industry-academic cluster model linking the central government's vision to create an innovative ecosystem and the specialized industry in the region serves as the biggest stepping stone. The Netherlands' innovation policy is expected to be more faithful to its role as Europe's "digital gateway" through regional development centered on the innovation cluster ecosystem and investment in job creation and new industries.

• 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.