• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.780-787
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• 2007

The Information and Communication Technologies (ICT) sector is undergoing a fundamental transformation. Determinant factors in this transformation process are deregulations and technological advancements. From the value chain perspective, industry convergence plays the most crucial role in the transformation of the established telecommunications and media industries. The objective of this paper is to validate the impact of convergence on the business performance using empirical data. To identify the various effects driven by industry convergence, we analyzed the relationships between firm's degree of convergence and the business performance using regression analysis. From the empirical result with the 2002-2005 data, it was found that the convergence across ICT sectors improves the business performance measured by Tobin's q significantly and this is only a recent phenomenon. These results are consistent with the conjecture that higher degree of convergence is becoming more associated with higher market value.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.3
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• pp.307-313
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• 2021

In this study, the comparative analysis, among the design standard value of distribution power, the calculated value from the measurement data of strand and the empirical data of the distribution line itself, have been performed for the elastic coefficients and linear expansion coefficients of distribution line conductors. The empirical values of elastic coefficients were lower about 10.6%(892kgf/mm²) than those of the design standard value of the distribution power and there were a little difference between the empirical values of linear expansion coefficients and the design standard value of the distribution power. From the above results, it could be concluded that the empirical values of conductor characteristics should be used in the dip design and installation of distribution line.

• Journal of the Korean Geographical Society
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• v.39 no.2
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• pp.212-228
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• 2004

This paper is concerned with an important aspect of regional income convergence, ${\beta}$-convergence, which refers to the negative relationship between initial income levels and income growth rates of regions over a period of time. The common research framework on ${\beta}$-convergence which is based on OLS regression models has two drawbacks. First, it ignores spatially autocorrelated residuals. Second, it does not provide any way of exploring spatial heterogeneity across regions in terms of ${\beta}$-convergence. Given that empirical studies on ${\beta}$-convergence need to be edified by spatial data analysis, this paper aims to: (1) provide a critical review of empirical studies on ${\beta}$-convergence from a spatial perspective; (2) investigate spatio-temporal income dynamics across the U.S. labor market areas for the last 30 years (1969-1999) by fitting spatial regression models and applying bivariate ESDA techniques. The major findings are as follows. First, the hypothesis of ${\beta}$-convergence was only partially evidenced, and the trend substantively varied across sub-periods. Second, a SAR model indicated that ${\beta}$-coefficient for the entire period was not significant at the 99％ confidence level, which may lead to a conclusion that there is no statistical evidence of regional income convergence in the US over the last three decades. Third, the results from bivariate ESDA techniques and a GWR model report that there was a substantive level of spatial heterogeneity in the catch-up process, and suggested possible spatial regimes. It was also observed that the sub-periods showed a substantial level of spatio-temporal heterogeneity in ${\beta}$-convergence: the catch-up scenario in a spatial sense was least pronounced during the 1980s.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.2_2
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• pp.219-232
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• 2022

The aim of this study is to provide the dynamic convergence index that reflected the inherent characteristics of the convergence phenomenon and utilized the nationally-funded R&D projects data, thereby suggesting useful information about the direction of the national convergence R&D strategy. The dynamic convergence index that we suggested was made of two indicators: persistency and diversity. From a time-series perspective, the persistency index, which measures the degree of continuous convergence of multidisciplinary nationally-funded R&D projects, and the diversity index, which measures the degree of binding with heterogeneous research areas. We conducted the empirical experiment with 151,248 convergence R&D projects during the 2015~2021 time period. The results showed that convergence R&D projects in both public health and life sciences appeared the highest degree of persistency. It was presumed that the degree of persistency has increased again due to the COVID-19 pandemic. Meanwhile, the degree of diversity has risen with combining with disciplinary such as materials, chemical engineering, and brain science areas to solve social problems including mental health, depression, and aging. This study not only provides implications for improving the concept and definition of dynamic convergence in terms of persistency and diversity for national convergence R&D strategy but also presented dynamic convergence index and analysis methods that can be practically applied for directing public R&D programs.

• Knowledge Management Research
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• v.20 no.1
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• pp.77-99
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• 2019

This study aims to examine validity and reliability of the convergence talent measurement. Based on the comprehensive literature review on convergence and convergence talent, we draw 4 main competencies (convergence cognitive, accomplishment, problem solving, and attitude) and 10 critical aspects (creative thinking, critical thinking, cooperation, communication, problem-solving, knowledge accessibility, resource utilization, trust, openness, caring) for building an effective convergence-oriented organization. A validity and reliability test survey were conducted for the analysis to investigate the convergence talent scale. With a data of 151 employees in diverse companies, the results show that the factors demonstrated acceptable levels of validity and reliability. The empirical evidences of our study are 10 key aspects comprising four main dimensions which suggest an available scale for measuring the convergent talent for enhancing convergence-oriented organizations. Our research contributes to the expansion of academic and practical implications on convergence, especially in convergence-oriented organizations.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.200-214
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• 2024

Amid the flood of data, social network analysis is beneficial in searching for its hidden context and verifying several pieces of information. This can be used for detecting the spread model of infectious diseases, methods of preventing infectious diseases, mining of small groups and so forth. In addition, community detection is the most studied topic in social network analysis using graph analysis methods. The objective of this study is to examine signed attributed social networks and identify the maximal balanced cliques that are both absolute and fair. In the same vein, the purpose is to ensure fairness in complex networks, overcome the "information cocoon" bottleneck, and reduce the occurrence of "group polarization" in social networks. Meanwhile, an empirical study is presented in the experimental section, which uses the personal information of 77 employees of a research company and the trust relationships at the professional level between employees to mine some small groups with the possibility of "group polarization." Finally, the study provides suggestions for managers of the company to align and group new work teams in an organization.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1812-1824
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• 2016

The neural dynamics underlying the causal network during motor planning or imagery in the human brain are not well understood. The lack of signal processing tools suitable for the analysis of nonlinear and nonstationary electroencephalographic (EEG) hinders such analyses. In this study, noise-assisted multivariate empirical mode decomposition (NA-MEMD) is used to estimate the causal inference in the frequency domain, i.e., partial directed coherence (PDC). Natural and intrinsic oscillations corresponding to the motor imagery tasks can be extracted due to the data-driven approach of NA-MEMD, which does not employ predefined basis functions. Simulations based on synthetic data with a time delay between two signals demonstrated that NA-MEMD was the optimal method for estimating the delay between two signals. Furthermore, classification analysis of the motor imagery responses of 29 subjects revealed that NA-MEMD is a prerequisite process for estimating the causal network across multichannel EEG data during mental tasks.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.667-678
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• 2021

The industrial life cycle theory was extended to the product life cycle theory and the corporate life cycle theory, but a conceptual life cycle was presented, and quantitative empirical evidence for this was insufficient. It is intended to improve appropriate resource planning and resource allocation by quantitatively predicting the industrial cycle and its position (age) in the cycle. Human resources, tangible assets, and industrial output analysis were conducted based on 28 years of actual data of 39 industries in Korea by applying the Gompertz model, which is a population ecology prediction model. By predicting with the Gompertz model, the coefficient of determination R2 value was 97% or more, confirming the high suitability with the actual cumulative sales value of the industry. A numerical model for calculating the life cycle of each industry, calculating the saturation of input resources for each industry, and diagnosing the financial stability of the industry was presented. These results will contribute to the decision-making of industrial policy officers for budget planning appropriately for each stage of industry development. Future research will apply the numerical model of this study to foreign national industries, complete an inter-industry convergence diagnostic model (e.g. ease of convergence, suitability of convergence, etc.) for renewal of fading industries.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.140-141
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• 2017

Big data analysis, in the large amount of data stored as the data warehouse which it refers the process of discovering meaningful new correlations, patterns, trends and creating new values. Thus, Big data analysis is an effective analysis of various big data that exist all over the world such as social big data, machine to machine (M2M) sensor data, and corporate customer relationship management data. In the big data era, it has become more important to effectively analyze not only structured data that is well organized in the database, but also unstructured big data such as the internet, social network services, and explosively generated web documents, e-mails, and social data in mobile environments. By the way, a meta analysis refers to a statistical literature synthesis method from the quantitative results of many known empirical studies. We reviewed a total of 750 samples among 50 studies published on the topic related as IDT between 2000 and 2017 in Korea.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.5
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• pp.451-461
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• 2014

This paper analyzed the degree of perception and performance of technology-humanities convergence in Korean SMEs. First, the meaning and activities of technology-humanities convergence were defined. Second, a survey data was collected from 277 Korean SMEs and analyzed from two perspectives-degree of performance and degree of perceived importance. Finally, based on the results, this paper signposted the important points to activate the technology-humanities convergence in SMEs.