• The Journal of Information Systems
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• v.27 no.1
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• pp.171-191
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• 2018

Purpose Corporate technology leakage is not only monetary loss, but also has a negative impact on the corporate image and further deteriorates sustainable growth. In particular, since SMEs are highly dependent on core technologies compared to large corporations, loss of technology leakage threatens corporate survival. Therefore, it is important for SMEs to "prevent and protect technology leakage". With the recent development of data analysis technology and the opening of public data, it has become possible to discover and proactively detect companies with a high probability of technology leakage based on actual company data. In this study, we try to construct profiles of enterprises with and without technology leakage experience through profiling analysis using data mining techniques. Furthermore, based on this, we propose a classification model that distinguishes companies that are likely to leak technology. Design/methodology/approach This study tries to develop the empirical model for prevention and protection of technology leakage through profiling method which analyzes each SME from the viewpoint of individual. Based on the previous research, we tried to classify many characteristics of SMEs into six categories and to identify the factors influencing the technology leakage of SMEs from the enterprise point of view. Specifically, we divided the 29 SME characteristics into the following six categories: 'firm characteristics', 'organizational characteristics', 'technical characteristics', 'relational characteristics', 'financial characteristics', and 'enterprise core competencies'. Each characteristic was extracted from the questionnaire data of 'Survey of Small and Medium Enterprises Technology' carried out annually by the Government of the Republic of Korea. Since the number of SMEs with experience of technology leakage in questionnaire data was significantly smaller than the other, we made a 1: 1 correspondence with each sample through mixed sampling. We conducted profiling of companies with and without technology leakage experience using decision-tree technique for research data, and derived meaningful variables that can distinguish the two. Then, empirical model for prevention and protection of technology leakage was developed through discriminant analysis and logistic regression analysis. Findings Profiling analysis shows that technology novelty, enterprise technology group, number of intellectual property registrations, product life cycle, technology development infrastructure level(absence of dedicated organization), enterprise core competency(design) and enterprise core competency(process design) help us find SME's technology leakage. We developed the two empirical model for prevention and protection of technology leakage in SMEs using discriminant analysis and logistic regression analysis, and each hit ratio is 65%(discriminant analysis) and 67%(logistic regression analysis).

• Journal of the Korean Institute of Gas
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• v.21 no.2
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• pp.41-49
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• 2017

Recently, as the number of facilities using chemicals is increasing, the amount of handling is rapidly increasing. However, chemical spills are occurring steadily, and if large quantities of chemicals are leaked in time, they are likely to cause major damage. These industrial complexes use information obtained from a number of sensors to detect and monitor leaking areas, and are used in industrial fields by applying existing fixed sensors to robots and drones. Therefore, it is necessary to propose a sensor placement method at the interface for rapid detection and response based on various leaking scenarios reflecting leaking conditions and environmental conditions of the chemical handling process. In this study, COMSOL was used to analyze the actual accident scenarios by applying the medium parameter to the case of chemical leaks. Based on the accident scenarios, the objective function is selected so that the velocity of each robot is calculated by attaching importance to each item of sensor detection probability, sensing time and sensing scenario number. We also confirmed the feasibility of this method of reliability analysis for unexpected leak accidents. Based on the above results, it is expected that it will be helpful to trace back the leakage source based on the concentration data of the portable sensor to be applied later.