• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.101-124
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• 2018

Recently, most of the technologies have been developed in various forms through the advancement of single technology or interaction with other technologies. Particularly, these technologies have the characteristic of the convergence caused by the interaction between two or more techniques. In addition, efforts in responding to technological changes by advance are continuously increasing through forecasting promising convergence technologies that will emerge in the near future. According to this phenomenon, many researchers are attempting to perform various analyses about forecasting promising convergence technologies. A convergence technology has characteristics of various technologies according to the principle of generation. Therefore, forecasting promising convergence technologies is much more difficult than forecasting general technologies with high growth potential. Nevertheless, some achievements have been confirmed in an attempt to forecasting promising technologies using big data analysis and social network analysis. Studies of convergence technology through data analysis are actively conducted with the theme of discovering new convergence technologies and analyzing their trends. According that, information about new convergence technologies is being provided more abundantly than in the past. However, existing methods in analyzing convergence technology have some limitations. Firstly, most studies deal with convergence technology analyze data through predefined technology classifications. The technologies appearing recently tend to have characteristics of convergence and thus consist of technologies from various fields. In other words, the new convergence technologies may not belong to the defined classification. Therefore, the existing method does not properly reflect the dynamic change of the convergence phenomenon. Secondly, in order to forecast the promising convergence technologies, most of the existing analysis method use the general purpose indicators in process. This method does not fully utilize the specificity of convergence phenomenon. The new convergence technology is highly dependent on the existing technology, which is the origin of that technology. Based on that, it can grow into the independent field or disappear rapidly, according to the change of the dependent technology. In the existing analysis, the potential growth of convergence technology is judged through the traditional indicators designed from the general purpose. However, these indicators do not reflect the principle of convergence. In other words, these indicators do not reflect the characteristics of convergence technology, which brings the meaning of new technologies emerge through two or more mature technologies and grown technologies affect the creation of another technology. Thirdly, previous studies do not provide objective methods for evaluating the accuracy of models in forecasting promising convergence technologies. In the studies of convergence technology, the subject of forecasting promising technologies was relatively insufficient due to the complexity of the field. Therefore, it is difficult to find a method to evaluate the accuracy of the model that forecasting promising convergence technologies. In order to activate the field of forecasting promising convergence technology, it is important to establish a method for objectively verifying and evaluating the accuracy of the model proposed by each study. To overcome these limitations, we propose a new method for analysis of convergence technologies. First of all, through topic modeling, we derive a new technology classification in terms of text content. It reflects the dynamic change of the actual technology market, not the existing fixed classification standard. In addition, we identify the influence relationships between technologies through the topic correspondence weights of each document, and structuralize them into a network. In addition, we devise a centrality indicator (PGC, potential growth centrality) to forecast the future growth of technology by utilizing the centrality information of each technology. It reflects the convergence characteristics of each technology, according to technology maturity and interdependence between technologies. Along with this, we propose a method to evaluate the accuracy of forecasting model by measuring the growth rate of promising technology. It is based on the variation of potential growth centrality by period. In this paper, we conduct experiments with 13,477 patent documents dealing with technical contents to evaluate the performance and practical applicability of the proposed method. As a result, it is confirmed that the forecast model based on a centrality indicator of the proposed method has a maximum forecast accuracy of about 2.88 times higher than the accuracy of the forecast model based on the currently used network indicators.

• Journal of Digital Convergence
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• v.13 no.11
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• pp.63-70
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• 2015

The fierce competition of mobile messenger services (MMS) allows MMS providers to perform a variety of marketing campaigns and business activities to enhance user loyalty. The applied model in this study is based on Oliver's four-stage loyalty model for the formation processes of user loyalty about MMS. While social network formation and service quality are the key elements of cognitive loyalty, positive mood and negative mood are the key components of affective loyalty in the study. Conative loyalty is captured by commitment. The data of 249 KakaoTalk users at least five times for three months is empirically tested based on the research model using partial least squares. The analysis of test identifies that positive feeling and commitment significantly influences behavioral loyalty, whereas negative feeling plays a significant role in inhibiting behavioral loyalty. The findings of this study show that social network formation and service quality significantly affect only positive feeling. The analysis results reveal several insights that can help MMS managers understand the roles of cognitive, affective, conative, and behavioral loyalty in the MMS environment.

• Journal of Digital Convergence
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• v.14 no.8
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• pp.185-194
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• 2016

Several game developers or publishers adopt open innovation strategies to reduce R&D costs and increase user loyalty about their games. User communities play an important role in increasing users' interests in the game because they can share game information and skills in user communities. In this regard, this study explored key antecedents of game user participation intention in user community. We developed a research model by integrating perceived risk into theory of planned action. The theoretical model was tested by using survey data collected from 110 "Suddenattack" game users. Partial least squares (PLS) was utilized to analysis the research model. The findings of this study indicate that both perceived usefulness and perceived enjoyment play an important role in forming attitude toward community. However, contrast to our expectations, perceived risk has no signifiant effect on perceived usefulness, perceived enjoyment, attitude toward community and participation intention. While attention toward community significantly influences community participation intention, social norms are not significantly related to it. The analysis results help game developers or publishers establish effective strategies and policies to increase user participation intention in user community.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.21-32
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• 2021

The government is focusing its attention and support on start-ups. Nevertheless, there is anxiety and fear about starting a business at the base of public awareness. Experienced as a way to overcome fear and difficulty. Few prior studies have been done on experience factors as ones influencing entrepreneurial intention. In this study, I studied whether the experience of successful entrepreneurship through cultural indirect experience affect the resolution of fear about establishing a business. Among the influencing factors on the entrepreneurial intention, business motivation, social support and awareness of entrepreneur were selected as independent variables. In addition, by applying the cultivation theory, the drama role models were set as a controlling variable. For empirical analysis, a survey was conducted targeting 399 ordinary persons. The hypothesis was tested through regression analysis using the SPSS 23 statistical package. The moderating effect was analyzed using Process Macro 3.5. Self-fulfillment, livelihood, economic motivation, social support, and awareness of entrepreneur are sub-factors of business motivation, And all of these had a positive significant effect on entrepreneurial intention. Among the significant variables, self-fulfillment was found to have the greatest effect. On the other hand, as a result of analyzing the moderating effect of the drama role model, it was found play a role in controlling between self-fulfillment and entrepreneurial intention, between livelihood and entrepreneurial intention, and between awareness of entrepreneur and entrepreneurial intention. Based on these research results, academic and practical implications were presented.

• The Journal of Industrial Distribution & Business
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• v.9 no.3
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• pp.31-43
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• 2018

Purpose - With the technology of the 4th industrial revolution, business models of agricultural sector are changing rapidly toward convergence and high value-added business models due to the 6th industrialization of agricultural. Our goals is to examine the 6th industrialization of agriculture, and then to apply the technology of the 4th industrial revolution to the 6th industrialization of agriculture, suggesting the possibility of future agriculture, and then linking the export competitiveness of agricultural products. Research design, data, and methodology - As the methodology, case studies and empirical analyzes were conducted as well as literature studies. The case analyses included tomatoes, pig breeding farms, and an empirical analysis was conducted using the AHP analysis by experts of the 6th industrialization. In addition, using 124 survey data, this study examined and analyzed the items of the 4th industrial revolution technology for the 6th industrialization of agriculture and the strengthening of export competitiveness. Results - Results showed that the technology of the 4th Industrial Revolution helped "6th industrialization of agriculture" and "the strengthening of export competitiveness" using two successful cases. The AHP analysis was also carried out, and it was found that the improvement of the technology in the 4th industrial revolution could contribute to the future industrialization as well as the 6th industrialization of agriculture. First of all, we looked many conditions were important and urgent. Among the technologies of the 4th industrial revolution, the mobile, big data were important. Moreover, it was recognized that linkage and convergence related efforts would greatly contribute to strengthening export competitiveness of agriculture such as price and quality competitiveness. Conclusions - The 4th industrial revolution such as hyper-connectivity, hyper-intelligence and hyper-predictability contribute greatly to the 6th industrialization of agriculture, and therefore it is essential to improve the competitiveness of the agricultural sector by using the technology of the 4th industrial revolution. In particular, based on analyses of the diamond model, the "demand conditions" was the most important factor for the activation of the 6th Industrialization, and then "related and supporting fields", "factor conditions" and "business context" were followed in order. The results of this study can be useful for policy, practical and academic sectors.

• Journal of Computing Science and Engineering
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• v.11 no.1
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• pp.32-38
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• 2017

Determining the correct word sense among ambiguous senses is essential for semantic analysis. One of the models for word sense disambiguation is the word space model which is very simple in the structure and effective. However, when the context word vectors in the word space model are merged into sense vectors in a sense inventory, they become typically very large but still suffer from the lexical scarcity. In this paper, we propose a word sense disambiguation method using word embedding that makes the sense inventory vectors compact and efficient due to its additive compositionality. Results of experiments with a Korean sense-tagged corpus show that our method is very effective.

• Korean Journal of Computational Design and Engineering
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• v.18 no.6
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• pp.407-416
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• 2013

Acting an important risk management tool for main contractors, pre-qualification has served a key marketing tool for subcontractors in various industries. Current industrial environment has required the time intensive pre-qualification ability to small and medium-size subcontractors as a matter of competitive business. In order to support the subcontractors, this paper proposes a database model for pre-qualification management system (PQMS) that automates the pre-qualification process by using information technologies. The modeling process consists of specifications for its requirements, functional modules, and a database model for the PQMS.

• Journal of information and communication convergence engineering
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• v.11 no.4
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• pp.235-240
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• 2013

Cloud systems are popular computing environment because they can provide easy access to computing resources for users as well as efficient use of resources for companies. The resources of cloud computing are heterogeneous and jobs have various characteristics. One such issue is effective job scheduling. Scheduling in the cloud system may be defined as a multiple criteria decision model. To address this issue, this paper proposes a priority-based two-step job scheduling algorithm. On the first level, jobs are classified based on preference. Resources are dedicated to a job if a deadline failure would cause severe results or critical business losses. In case of only minor discomfort or slight functional impairment, the job is scheduled using a best effort approach. On the second level, jobs are allocated to adequate resources through their priorities that are calculated by the analytic hierarchic process model. We then analyze the proposed algorithm and make a scheduling example to confirm its efficiency.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.2
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• pp.151-162
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• 2014

This study proposes an optimization model to plan the patient distribution and medical resource allocation considering the diverse characteristics of disaster. For reflecting the particularity of disaster response, we configured a few scenarios such as availability of emergency surgery of non-major medical staff and the change in number of patients estimated reflecting the uncertainty, urgency and convergence of disaster. And we finally tested the effects of the scenarios' combination on the objective function defined as maximum number of survival patients. Our experimental results are expected to highlight the significance of the proposed model as well as the applicability of scenarios under disaster response.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.27-39
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• 2018

Since the global positioning system (GPS) has been included in mobile devices (e.g., for car navigation, in smartphones, and in smart watches), the impact of personal GPS log data on daily life has been unprecedented. For example, such log data have been used to solve public problems, such as mass transit traffic patterns, finding optimum travelers' routes, and determining prospective business zones. However, a real-time analysis technique for GPS log data has been unattainable due to theoretical limitations. We introduced a machine learning model in order to resolve the limitation. In this paper presents a new, three-stage real-time prediction model for a person's daily route activity. In the first stage, a machine learning-based clustering algorithm is adopted for place detection. The training data set was a personal GPS tracking history. In the second stage, prediction of a new person's transient mode is studied. In the third stage, to represent the person's activity on those daily routes, inference rules are applied.