• Proceedings of the Technology Innovation Conference
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• 1997.07a
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• pp.222-223
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• 1997

By analyzing the successful innovation case of potato microtuber mass production technology, a representative case of technology-push type creative innovation in an imitation oriented research culture, this paper attempts to figure out conceptual model of creative innovation that is initiated by the public laboratories in catching-up country, Stages of creative innovation can be divided into the internal R&D stage and the external commercialization stage. Success of the internal R&D stage depended on autonomy to secure creative research idea and commitment of individual researchers. Psychological pressure evoked from sportlights of mass media and commitment of sponsor increased the intensity of research efforts of the researcher Recognition of research problem and its significance was intensified by site visits of agricultural fields, and the recognized higher impacts of expected research results and knowledge creation achieved were a fundamental source of self-motivation. In the stage of commercialization stage, various legal, socio-economic, and psychological barriers were confronted. In a catching-up country lacking of experiences of creative innovation, creative innovation process can be regarded as a barrier elimination and cultural revolution process. Among the barriers, psychological refusal of farmers to corn-sized potato seeds was critical, which finally enforced to further researches to enlarge the size of potato seeds. In addition, the researcher has concentrated his research efforts in one specialized research area by getting a series of similar research project funds rather than diversification. It was lucky for him to have a chance to carry out a series of similar researches in one research area during the last 10 years. In getting research funds from government and private companies continuously in one research area, both internal and external promoters played significant roles.

• Journal of Distribution Science
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• v.14 no.2
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• pp.93-101
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• 2016

Purpose - This study aims to advance our knowledge about factors influencing technical startup performance through analysing technical startup process empirically. This study was conducted to focus on industry structure(industry growth rate, competitive intensity, and enter barriers), technology characteristics(technical excellence and wide range of technical application), and the performance in the technology-based start-ups. Specifically, analyzing moderating effect of technology-marketing strategy, this studied how moderating variables affect technical startup performance under industry structure. Research design, data, and methodology - The subject of this study was technology-based start-ups company that received technology transfer from public organization. The development of the paper model is based on the literature of the preceding research analysis in technology commercialization, performance of technology-based start-ups, and marketing strategy. This study has a construct that was defined in the previous studies, such that technology marketing strategy was defined into the two ways of being broad or narrow in strategic application. From November 3. 2015 to December 22, 220 questionnaires were distributed with targeting to start-up companies in technology-based. 188 responses were collected for empirical analysis except the missing and wrong value responses. This data were used for structural equation modeling and regression analysis. Results - The results of this study are as follows. First, as industry structure variables influencing on performance(technical, financial) of technology-based start-ups, industry growth rate, competitive intensity and enter barriers of variables were verified; high growth rate has more positive effect on performance than low growth rate, competitive low intensity has more positive effect on performance than competitive high intensity, low enter barriers have more positive effect on performance than high enter barriers. Second, as technology characteristics variables influences on the performance(technical, financial) of technology-based start-ups, technical excellence and wide range of technical application of variables were verified ; technical high-excellence has more positive effect on performance than technology low-excellence, wide range of technical application has more positive effect on performance than narrow range of technical application. We also find that technology marketing strategy(broad/narrow) in moderating factors on performance (technical, financial) is as follows. Analyzing the moderating effect depending on technology marketing strategy(broad/narrow), application of technology, and the types of technology strategy(broad/narrow) were revealed that broad marketing strategy had a more significant effect on performance of technology-based start-ups. With AMOS, the relevancy of the study model revealed higher for broad technology-marketing strategy than narrow technology marketing strategy, and the explanatory power revealed to be 6.4% higher in broad marketing strategy than narrow marketing strategy. Conclusions - This study confirmed that industry structure and technology characteristics are important factors influencing the performance of technology-based start-ups. Technology-marketing strategy affects the performance of technology-based start-ups between industry structure and technology characteristics. According to additional analysis, moderating variables and technology-marketing strategy are important factors influencing the performance of technology-based start-ups under industry structure and technology characteristics. Broad type of technology-marketing strategy has more attractive industry structure and excellent technology characteristics than narrow types of technology-marketing.

• Korean Journal of Construction Engineering and Management
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• v.16 no.4
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• pp.118-128
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• 2015

Recently BIM technology has been expanded for using in construction project. Due to the high-cost of BIM infrastructure development, lack of regulations, lack of process and so forth, usage of BIM has been delayed than initial expectations. In design phase, especially, collaboration based on BIM system has been a key factor for successful next generation building project. Through the analysis of current research trends about IT technologies, virtualization and BIM service, data exchange such as drawings, 3D model, object data, properties using cloud computing and virtual server system is defined as a most successful solution. The purpose of this study is to enable the cloud computing BIM server to provide several main functions such as editing models, 3D model viewing and checking, mark-up and snapshot in high-performance quality by proper design of VDI system. Concurrent client connection performance is a main technical index of VDI. Through testing of test-bed server client, developed VDI system's multi-connect control is evaluated. Performance-test result of BIM server VDI effect to development direction of cloud computing BIM service for commercialization.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.1-22
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• 2017

Analysis of future business or investment opportunities, such as business feasibility analysis and company or technology valuation, necessitate objective estimation on the relevant market and expected sales. While there are various ways to classify the estimation methods of these new sales or market size, they can be broadly divided into top-down and bottom-up approaches by benchmark references. Both methods, however, require a lot of resources and time. Therefore, we propose a data-based intelligent demand forecasting system to support evaluation of new business. This study focuses on analogical forecasting, one of the traditional quantitative forecasting methods, to develop sales forecasting intelligence systems for new businesses. Instead of simply estimating sales for a few years, we hereby propose a method of estimating the sales of new businesses by using the initial sales and the sales growth rate of similar companies. To demonstrate the appropriateness of this method, it is examined whether the sales performance of recently established companies in the same industry category in Korea can be utilized as a reference variable for the analogical forecasting. In this study, we examined whether the phenomenon of "mean reversion" was observed in the sales of start-up companies in order to identify errors in estimating sales of new businesses based on industry sales growth rate and whether the differences in business environment resulting from the different timing of business launch affects growth rate. We also conducted analyses of variance (ANOVA) and latent growth model (LGM) to identify differences in sales growth rates by industry category. Based on the results, we proposed industry-specific range and linear forecasting models. This study analyzed the sales of only 150,000 start-up companies in Korea in the last 10 years, and identified that the average growth rate of start-ups in Korea is higher than the industry average in the first few years, but it shortly shows the phenomenon of mean-reversion. In addition, although the start-up founding juncture affects the sales growth rate, it is not high significantly and the sales growth rate can be different according to the industry classification. Utilizing both this phenomenon and the performance of start-up companies in relevant industries, we have proposed two models of new business sales based on the sales growth rate. The method proposed in this study makes it possible to objectively and quickly estimate the sales of new business by industry, and it is expected to provide reference information to judge whether sales estimated by other methods (top-down/bottom-up approach) pass the bounds from ordinary cases in relevant industry. In particular, the results of this study can be practically used as useful reference information for business feasibility analysis or technical valuation for entering new business. When using the existing top-down method, it can be used to set the range of market size or market share. As well, when using the bottom-up method, the estimation period may be set in accordance of the mean reverting period information for the growth rate. The two models proposed in this study will enable rapid and objective sales estimation of new businesses, and are expected to improve the efficiency of business feasibility analysis and technology valuation process by developing intelligent information system. In academic perspectives, it is a very important discovery that the phenomenon of 'mean reversion' is found among start-up companies out of general small-and-medium enterprises (SMEs) as well as stable companies such as listed companies. In particular, there exists the significance of this study in that over the large-scale data the mean reverting phenomenon of the start-up firms' sales growth rate is different from that of the listed companies, and that there is a difference in each industry. If a linear model, which is useful for estimating the sales of a specific company, is highly likely to be utilized in practical aspects, it can be explained that the range model, which can be used for the estimation method of the sales of the unspecified firms, is highly likely to be used in political aspects. It implies that when analyzing the business activities and performance of a specific industry group or enterprise group there is political usability in that the range model enables to provide references and compare them by data based start-up sales forecasting system.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.6
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• pp.49-62
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• 2018

The purpose of this study is to develop an assessment index for the selection of promising start-ups, which will enhance the efficiency of program that support start-ups. In order to develop assessment models for selecting start-ups, three major research steps were conducted. First, this study attempted to theoretically redefine the assessment index from the perspective of the Balanced Scorecard (BSC) through a literature review. Second, major assessment index were derived using Delphi technique for experts in start-up areas. Third, weights were derived by applying AHP technique to calculate the importance of each index. The results of this study are summarized as follows. First, this study attempted to apply the assessment model for selecting start-ups from the Balanced Scorecard (BSC) view through the previous study review. Second, the final major questions were derived with sufficient opinions collected and structured survey of leading start-up experts in areas related to research subjects and elicited the most representative questions. Third, the results of applying the weights of the main selected assessment index, commercialization viewpoint is the most priority, followed by market view, technology development viewpoint, and organizational capability viewpoint. In the middle section, th ability to make products in the commercialization viewpoint, market competitiveness in the market, product discrimination capacity in the technology development perspective, and the ability of the entrepreneur in the organizational capacity perspective were important. Overall important items were found to be in the order of the capabilities of entrepreneurs, market competitiveness, product fire capability, and product discrimination. The importance of small items was highest priority for comparative excellence of competing products, and the degree of marketability, capacity of entrepreneurship, ability to raise capital, desire for entrepreneurship, and passion were shown. The results of this study presented a conceptual alternative to the preceding study on the development of existing selection assessment indexes. And it provides meaningful and important implications as an attempt to develop more sophisticated indicators by overcoming the limitations of empirical research on only some of the evaluation metrics.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.2
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• pp.370-379
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• 2018

The Korean government announced plans for the International Science Business Belt as a spatial area for promoting the linkage between scientific knowledge and commercialization in 2009. R&D and entrepreneurial activities are essential for the success of the International Science Business Belt. In particular, prioritizing the types of businesses is critical at the cluster establishment stage in that this largely affects the features and development of clusters comprising the International Science Business Belt. This research aims to predict the entry and growth of firms that specialize in four industrial clusters, including Big Science Cluster, Frontier Cluster, ICT Cluster, and Bio-Healthcare Cluster. For this purpose, we employ the Swann & Prevezer's industrial clustering model to identify sectors that affect the establishment and growth of industrial clusters in the International Science Business Belt, focusing on ICT, Bio-Healthcare and Frontier clusters. Data was collected from the 2014 Korean Innovation Survey (KIS) and University Alimi for the ICT cluster, 2014 National Bio Industry Survey and University Alimi for the Bio-Healthcare Cluster, and the 2015 National Nano Convergent Industry Survey and Annual Report of Nano Technology for the Frontier cluster. Empirical results show that the ICT service sector, bio process/equipment sector, and Nano electronic sector promote clustering in other sectors. Based on the analysis results, we discuss several policy implications and strategies that can attract relevant firms for the development of industrial clusters.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.2
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• pp.127-135
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• 2020

The purpose of this paper is to classify individual startups by growth stage based on data-based quantitative criteria. This is to provide a basis for systematic support for government startups based on accurate statistics on the startup growth process. This startups were the TIPS (Tech Incubator Program for Startup) support company, which used a relatively reliable startup. We found seed money to complete MVP (Minimum Viable Product) within 1.5 years after establishment, verified PMF (Product-Market Fit) within 1 year, attracted Series A investment within 2.5 years after establishment, and successfully commercialized it. It attracted Series B investment for stable growth within 1.5 years (Series B investment within 4 years from start-up). The results of the study, the division of government programs that support stage-based startup commercialization, that is, within three years and within seven years of establishment, is significant to date. Three directions are suggested for future research. First, develop indicators for monitoring startup growth stages. Second, it continuously updates the annual changes and tracks the growth stages of individual startups. Third, we discover the successful growth law of technology-based startups by applying in-depth case analysis of successful startups to the model.

• Journal of Technology Innovation
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• v.20 no.2
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• pp.161-187
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• 2012

Recently, environmentally friendly technology are becoming important due to reconsideration about climate change and environmental pollution. In addition, as well as technical skills and social interaction through an analysis of the nonlinear transition management and policy implementation are emerging. This study of the development of photovoltaic industry in Korea 10 years analyze with strategic niche management (SNM) based on the theoretical and multi-layered perspective (MLP) is used as the analytical framework. Choose the gerverment-support project for niche technology, through a process of quantifying and alnalyze the phase transition to Regime with the numerical method and policy vision, learning effects, and network that key elements of SNM, MLP. Through the analysis of the photovoltaic industry technology-commercialization phase was investigated. This conventional overall and step-by-step model for technical management is proposed to replace exiting linear and narrow method and through the case study its validity was confirmed.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.177-198
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• 2018

Recently, as economic property it has become necessary to acquire and utilize the framework for water resource measurement and performance management as the property of water resources changes to hold "public property". To date, the evaluation of water technology has been carried out by feasibility study analysis or technology assessment based on net present value (NPV) or benefit-to-cost (B/C) effect, however it is not yet systemized in terms of valuation models to objectively assess an economic value of technology-based business to receive diffusion and feedback of research outcomes. Therefore, K-water (known as a government-supported public company in Korea) company feels the necessity to establish a technology valuation framework suitable for technical characteristics of water resources fields in charge and verify an exemplified case applied to the technology. The K-water evaluation technology applied to this study, as a public interest goods, can be used as a tool to measure the value and achievement contributed to society and to manage them. Therefore, by calculating the value in which the subject technology contributed to the entire society as a public resource, we make use of it as a basis information for the advertising medium of performance on the influence effect of the benefits or the necessity of cost input, and then secure the legitimacy for large-scale R&D cost input in terms of the characteristics of public technology. Hence, K-water company, one of the public corporation in Korea which deals with public goods of 'water resources', will be able to establish a commercialization strategy for business operation and prepare for a basis for the performance calculation of input R&D cost. In this study, K-water has developed a web-based technology valuation model for public interest type water resources based on the technology evaluation system that is suitable for the characteristics of a technology in water resources fields. In particular, by utilizing the evaluation methodology of the Institute of Advanced Industrial Science and Technology (AIST) in Japan to match the expense items to the expense accounts based on the related benefit items, we proposed the so-called 'K-water's proprietary model' which involves the 'cost-benefit' approach and the FCF (Free Cash Flow), and ultimately led to build a pipeline on the K-water research performance management system and then verify the practical case of a technology related to "desalination". We analyze the embedded design logic and evaluation process of web-based valuation system that reflects characteristics of water resources technology, reference information and database(D/B)-associated logic for each model to calculate public interest-based and profit-based technology values in technology integrated management system. We review the hybrid evaluation module that reflects the quantitative index of the qualitative evaluation indices reflecting the unique characteristics of water resources and the visualized user-interface (UI) of the actual web-based evaluation, which both are appended for calculating the business value based on financial data to the existing web-based technology valuation systems in other fields. K-water's technology valuation model is evaluated by distinguishing between public-interest type and profitable-type water technology. First, evaluation modules in profit-type technology valuation model are designed based on 'profitability of technology'. For example, the technology inventory K-water holds has a number of profit-oriented technologies such as water treatment membranes. On the other hand, the public interest-type technology valuation is designed to evaluate the public-interest oriented technology such as the dam, which reflects the characteristics of public benefits and costs. In order to examine the appropriateness of the cost-benefit based public utility valuation model (i.e. K-water specific technology valuation model) presented in this study, we applied to practical cases from calculation of benefit-to-cost analysis on water resource technology with 20 years of lifetime. In future we will additionally conduct verifying the K-water public utility-based valuation model by each business model which reflects various business environmental characteristics.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.