• Journal of the Korean Society of Costume
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• v.67 no.4
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• pp.58-70
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• 2017

This study examined the characteristics of generative design methodology, and it selected and analyzed 11 sites that provide design development services using generative methodology, INPUT data were classified into design form factor based on the choices of each and the variable form associated with each participant's movement. In designing the algorithms, designers set the meth- od and range of participation according to the characteristics of the design, and linked them with design components. This provided the study with a discriminative design process. This resulted in a type with morphological characteristics and a type that emphasized epidermal formability. The generative design methodology has value as an alternative process in the field of fashion where the utilization of digital technology is becoming universal based on contingency and variability. The results of this study can be used as basic data for the study of personalized design through the extension of methodology of digital-based fashion design and generative methodology.

• Structural Engineering and Mechanics
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• v.65 no.6
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• pp.751-760
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• 2018

Damages in concrete structures due to aging and other factors could be a serious and immense matter. Making the best selection of the most viable and practical repairing and strengthening techniques are relatively difficult tasks using traditional methods of structural analyses. This is due to the fact that the traditional methods used for assessing aging structure are not fully capable when considering the randomness in strength, loads and cost. This paper presents a reliability-based methodology for assessing reinforced concrete members. The methodology of this study is based on probabilistic analysis, using statistics of the random variables in the performance function equations. Principles of reliability updating are used in the assessment process, as new information is taken into account and combined with prior probabilistic models. The methodology can result in a reliability index ${\beta}$ that can be used to assess the structural component by comparing its value with a standard value. In addition, these methods result in partial safety factor values that can be used for the purpose of strengthening the R/C elements of the existing structure. Calculations and computations of the reliability indices and the partial safety factors values are conducted using the First-order Reliability Method and Monte Carlo simulation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.757-769
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• 2013

Intangible assets for construction engineering consulting firms are very important for their valuation, because engineering consulting is typical knowledge-based business which creates value based on technical expertise and human resources. This paper presents the intangible asset classification model based on the concept of value creation in construction engineering consulting firm and proposes intangible asset valuation methodology using System Dynamics and survey data. Utilization of the valuation methodology presented in this paper would increase the public awareness of intangible assets in construction engineering consulting firm and, thus, contribute to the growth of the engineering consulting industry by realistic and accurate valuation of intangible assets.

• Journal of the Korean Society for information Management
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• v.25 no.4
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• pp.245-267
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• 2008

This study is to develope a user-based economic valuation methodology and model for the economic analysis on the DDS of foreign journals. For this study, the sampling data on the annual subscribed journals by K institution was used and the online questionnaire was used to collect data. There are three aspects of the economic value of DDS journals was classified as use value, non-use value, and expectancy value. We suggested the income and market approach to measure its economic use value. To estimate the its value by individual users, this study applied a contingent valuation method and designed the imaginary scenarios.

• Transactions of Materials Processing
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• v.9 no.5
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• pp.512-525
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• 2000

Designers are frequently faced with multiple quality issues in injection molded parts. These issues are usually In conflict with each other, and thus tradeoff needs to be made to reach a final compromised solutions. The objective of this study is to develop an automated injection molding design methodology, whereby part defects such as warpage and weld line are optimized. The features of the proposed methodology are as follows: first, Utility Function approach is applied to transform the original multiple objective problem into single objective problem. Second is an implementation of a direct search-based Injection molding optimization procedure with automated consideration of process variation. The Space Reduction Method based on Taguchi's DOE(Design Of Experiment) is used as a general optimization tool in this study. The computational experimental verification of the methodology was partially carried out for a can model of Cavallero Plastics Incorporation, U. S. A. Applied to production, this study will be of immense value to companies in reducing the product development time and enhancing the product quality.

• Journal of The Korean Association For Science Education
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• v.28 no.3
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• pp.253-268
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• 2008

This paper has two major purposes. One is to introduce the essentialist methodology as a way to articulate subjective aspects of human beings (e.g. teachers' personal values and concerns, philosophies, subjective experiences, etc.) at a deeper level. And the other is to present two portraits, as examples, of science teachers who actively address socioscientiifc issues (SSI) out of their own motivations. The primary data source was consecutive in-depth interviews with two science teachers, Jenna and Thomas, and the interviews were conducted on the basis of the principle of the "participant as ally" (Witz, 2006). The articulation based on the essentialist methodology shows that teachers' deep-rooted values and convictions often play a significant role as a personal social capital enough to expand their teaching practice (i.e. teaching SSI). Namely, this study confirms that teachers who are motivated out of their own convictions are likely to actively develop their own personal practical knowledge, and to implement particular topics or teaching strategies.

• International Journal of Quality Innovation
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• v.7 no.3
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• pp.82-91
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• 2006

In order to achieve sustainable growth in the era of global competitiveness, a speedy and flexible strategy is needed in the fast changing management environment. For this purpose, strengthening the core confidence in the organization along with acquiring competitive advantages that cannot be easily copied by competitors should be done based on dealing with needs from customers and markets positively. In this study, the creative value innovation strategy and creative value design methodology will be presented to improve company competitiveness.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.1
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• pp.26-33
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• 2006

The potential needs as well as visible needs of customer should be considered in order to research and analyze of the customer data. The methods to analyze customer data is classified into customer segmentation, clustering analysis model, forecasting customer response probability model, analysis of the customer break rate model and new customer analysis model by the purpose. In this study, we developed the CW-CLV (Correlation Weight Customer Lifetime Value)method that used AHP(Analytic Hierarchy Process)rule for enhance the reliability of customer data and quantitative analysis of the customer segmentation, based on CLV(Customer Lifetime Value). We suggest to new variables and methodology from determined CW-CLV coefficients, because all of companies respect to the diversified customers classification and complexity of consumers needs. Finally, we unfolded any company's scheduling added new methodology using simulation and leaded conclusion about the new methodology.

• Journal of Distribution Science
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• v.22 no.2
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• pp.51-61
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• 2024

Purpose: This comprehensive study delves into the intricate relationship between customer engagement, perceived risk, and perceived value within China's burgeoning e-commerce livestreaming sector. It focuses on how different customer engagement types in livestreaming influence their perception of value and risk. Research Design, Data, and Methodology: Adopting a convenience sampling approach, this research scrutinizes data collected from 852 consumers actively involved in e-commerce livestreaming shopping. Participants provided their insights through a meticulously designed questionnaire survey. Structural equation modeling helped examine the interplay between customer engagement, perceived risk, and value. Results: Significant impacts of customer engagement on perceived value and risk were found. Observation-based, conversation-based, and action-based engagements enhance perceived risk, while conversation-based and action-based engagement reduce perceived risk. Interestingly, observation-based engagement did not significantly affect perceived risk. The study also uncovered that perceived risk negatively impacts perceived value. Conclusions: The research offers insights into customer behavior and value creation in e-commerce livestreaming. It underscores how different engagement types affect perceived value and risk, aiding e-commerce platforms and businesses in strategy development to improve customer experience and minimize risks, enhancing perceived value in this dynamic sector. Enhances understanding of customer engagement dynamics in China's e-commerce livestreaming, guiding strategic development.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.