• Information Systems Review
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• v.20 no.4
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• pp.79-96
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• 2018

This study started by focusing on the internalization of the technology appraisal model into the credit rating model to increase the discriminative power of the credit rating model not only for SMEs but also for all companies, reflecting the items related to the financial stability of the enterprises among the technology appraisal items. Therefore, it is aimed to verify whether the technology appraisal model can be applied to identify high-stability SMEs in advance. We classified companies into industries (manufacturing vs. non-manufacturing) and the age of company (initial vs. non-initial), and defined as a high-stability company that has achieved an average debt ratio less than 1/2 of the group for three years. The C5.0 was applied to verify the discriminant power of the model. As a result of the analysis, there is a difference in importance according to the type of industry and the age of company at the sub-item level, but in the mid-item level the R&D capability was a key variable for discriminating high-stability SMEs. In the early stage of establishment, the funding capacity (diversification of funding methods, capital structure and capital cost which taking into account profitability) is an important variable in financial stability. However, we concluded that technology development infrastructure, which enables continuous performance as the age of company increase, becomes an important variable affecting financial stability. The classification accuracy of the model according to the age of company and industry is 71~91%, and it is confirmed that it is possible to identify high-stability SMEs by using technology appraisal items.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.21-35
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• 2023

Expectations surrounding generative AI technology and its profound ramifications are sweeping across various industrial domains. Given the anticipated pivotal role of the startup ecosystem in the utilization and advancement of generative AI technology, it is imperative to cultivate a deeper comprehension of the present state and distinctive attributes characterizing venture capital (VC) investments within this domain. The current investigation delves into South Korea's landscape of VC investment deals and prognosticates the projected VC investments by juxtaposing these against the United States, the frontrunner in the generative AI industry and its associated ecosystem. For analytical purposes, a compilation of 286 investment deals originating from 117 U.S. generative AI startups spanning the period from 2008 to 2023, as well as 144 investment deals from 42 South Korean generative AI startups covering the years 2011 to 2023, was amassed to construct new datasets. The outcomes of this endeavor reveal an upward trajectory in the count of VC investment deals within both the U.S. and South Korea during recent years. Predominantly, these deals have been concentrated within the early-stage investment realm. Noteworthy disparities between the two nations have also come to light. Specifically, in the U.S., in contrast to South Korea, the quantum of recent VC deals has escalated, marking an augmentation ranging from 285% to 488% in the corresponding developmental stage. While the interval between disparate investment stages demonstrated a slight elongation in South Korea relative to the U.S., this discrepancy did not achieve statistical significance. Furthermore, the proportion of VC investments channeled into generative AI enterprises, relative to the aggregate number of deals, exhibited a higher quotient in South Korea compared to the U.S. Upon a comprehensive sectoral breakdown of generative AI, it was discerned that within the U.S., 59.2% of total deals were concentrated in the text and model sectors, whereas in South Korea, 61.9% of deals centered around the video, image, and chat sectors. Through forecasting, the anticipated VC investments in South Korea from 2023 to 2029 were derived via four distinct models, culminating in an estimated average requirement of 3.4 trillion Korean won (ranging from at least 2.408 trillion won to a maximum of 5.919 trillion won). This research bears pragmatic significance as it methodically dissects VC investments within the generative AI domain across both the U.S. and South Korea, culminating in the presentation of an estimated VC investment projection for the latter. Furthermore, its academic significance lies in laying the groundwork for prospective scholarly inquiries by dissecting the current landscape of generative AI VC investments, a sphere that has hitherto remained void of rigorous academic investigation supported by empirical data. Additionally, the study introduces two innovative methodologies for the prediction of VC investment sums. Upon broader integration, application, and refinement of these methodologies within diverse academic explorations, they stand poised to enhance the prognosticative capacity pertaining to VC investment costs.

• 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.

• Journal of the Korean Institute of Gas
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• v.12 no.2
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• pp.12-17
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• 2008

The In recent years, the need for more fuel-efficient and lower-emission vehicles has driven the technical development of alternative fuels such as LPG (Liquefied Petroleum Gas) which is able to meet the limits of better emission levels without many modifications to current engine design. LPG has a high vapor pressure and lower viscosity and surface tension than diesel and gasoline fuels. These different fuel characteristics make it difficult to directly apply the conventional gasoline or diesel fuel pump. In this study, experiments are performed to get initial performance and efficiency of the fuel pump under different condition of the temperature and composition of fuel. The characteristics of vane type fuel pump were investigated to access the applicability on LPLi engine.

• Information Systems Review
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• v.25 no.2
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• pp.57-76
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• 2023

This study classifies the success of creators and YouTubers who have created channels on YouTube recently, which is the most influential digital platform. Based on the actual information disclosure of YouTubers who are in the field of science and technology category, video upload cycle, video length, number of selectable multilingual subtitles, and information from other social network channels that are being operated, the success of YouTubers using machine learning was classified and analyzed, which is the closest to the YouTube revenue structure. Our findings showed that neural network algorithm provided the best performance to predict the success or failure of YouTubers. In addition, our five factors contributed to improve the performance of the classification. This study has implications in suggesting various approaches to new individual entrepreneurs who want to start YouTube, influencers who are currently operating YouTube, and companies who want to utilize these digital platforms. We discuss the future direction of utilizing digital platforms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.450-454
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• 2003

Most small and medium IT enterprises have problems because of the lack of resources, Technology, fund, marketing, advertisement etc. Globalization and Internationalization make the small and medium IT ventures to advance into the overseas market. In korea, Small and medium size IT company have a excellent technology and good potential for the future value. but they have small domestic market and critical competitiveness. So, this paper analysis, reviewed and suggestions to Internationalization.

• Knowledge Management Research
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• v.20 no.2
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• pp.197-216
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• 2019

The Korean government has been focusing on supporting technology startups to solve social and economic problems such as low growth, declining economic growth rate, rising youth unemployment rate and lack of new growth engine. Although the failure rate of young technology startups is very high, relevant researches are still scant. On the basis of previous researches, this study has identified four growth factors of technology startups: characteristics of entrepreneurs, technical superiority and originality of business items, focused marketing strategy, and follow-up government support projects. Five young technology startup cases were selected and analyzed in the cosmetics industry which were supported by the Six-month Challenge Platform project of Chungbuk Creation Economic Innovation Center. The main findings of the case study were as follows: First, product development through inhouse R&D rather than external contracted R&D was beneficial to acquiring follow-up government support projects and external investment. Second, choosing a small niche market and concentrating marketing efforts on the target market had a positive effect on firm performance. And, third, relevance of entrepreneurs' college major and technological originality of business items were confirmed to influence firm performance positively in the early stage. The results are expected to help young technology startups survive successfully and establish a foothold for growth in their early stage.

• Korean small business review
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• v.43 no.4
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• pp.117-154
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• 2021

With the arrival of the Internet age, which is characterised by interconnection, the importance of networks is increasing in entrepreneurial activities and networked incubation (NI) has emerged as a mainstream concept in business incubation (BI). However, detailed studies on operation models of NI have been scarcely conducted. In this respect, this paper suggests a knowledge-focused networked incubation (KNI) model optimised for the study BI was established by theoretical analysis, and has been applied. The initial diagnosis of the effectiveness of the KNI model was also conducted through descriptive statistics, case studies, and the Industrial Depth Interview (IDI). This study is significant in that it has elaborated an NI operation model that meets the down-to-earth needs of incubatees and also is universally applicable.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.237-258
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• 2022

As the global market environment changes, the domestic ICT industry has a growing influence on the world economy. This industry is regarded as an important driving force in the national economy from a technological and social point of view. In particular, small and medium-sized enterprises (SMEs) in the ICT industry are regarded as essential actors of domestic economic development in terms of company diversity, technology development and job creation. However, since it is small compared to large-sized enterprises, it is difficult for SMEs to survive with a differentiated strategy in an incomplete and rapidly changing environment. Therefore, SMEs must make a lot of efforts to improve their own capabilities, and the government needs to provide the desirable help suitable for corporate internal resources so that they can continue to be competitive. This study classifies the types of ICT SMEs participating in government support programs, and analyzes the relationship between resources and performance creation of each type. The data from the "ICT Small and Medium Enterprises Survey" conducted annually by the Ministry of Science and ICT was used. In the first stage, ICT SMEs were clustered based on common factors according to their experiences with government support programs. Three clusters were meaningfully classified, and each cluster was named "active participation type," "initial support type," and "soloist type." As a second step, this study compared the characteristics of each cluster through profiling analysis for each cluster. The third step carried out in this study was to find out the mechanism of R&D performance creation for each cluster through regression analysis. Different factors affected performance creation for each cluster, and the magnitude of the influence was also different. Specifically, for "active participation type", "current manpower", "technology competitiveness", and "R&D investment in the previous year" were found to be important factors in creating R&D performance. "Initial support type" was identified as "whether or not a dedicated R&D organization exists", "R&D investment amount in the previous year", "Ratio of sales to large companies", and "Ratio of vendors supplied to large companies" contributed to the performance. Lastly, in the case of "soloist type", "current workforce" and "future recruitment plan", "technological competitiveness", "R&D investment", "large company sales ratio", and "overseas sales ratio" showed a significant relationship with the performance. This study has practical implications of showing what strategy should be established when supporting SMEs in the future according to the government's participation in the startup program and providing a guide on what kind of support should be provided.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.2 no.3
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• pp.61-102
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• 2007

This study empirically analyzed how human resource management(HRM) practices affect the performance of venture firms using The results of the study are as follows: First of all, several HRM practices were found to affect organizational performance significantly. Specifically, ${(1)}$ recruitment and selection practices were negatively related to turnover, which seemed to mean that effective staffing including development of good recruitment pools and rigorous selection process lower turnover, and ${(2)}$ training and development, compensation, and labor-management relations were positively related to subjective performance of the firms, which implied that as the venture firms provide more opportunities of training and development to employees, provide compensation on the basis of performance, and develop cooperative labor-management relations, the subjective performance of the venture firms Increases. Secondly, negative interaction effects were found to exist between competitive strategies and HRM practices on organizational performance. Specifically, ${(1)}$ the interaction between differentiation strategy and compensation were significantly related to turnover, ${(2)}$ HRM planning and training and development interacted with differentiation strategy to significantly affect subjective organizational performance, and ${(3)}$ HRM planning, selection, training and development, compensation and communication practices interacted with technology innovation strategy to affect subjective organizational performance. So far, there have not been many studies which deal with HRM practices of venture firms in Korea. Thus, it is hoped that this study stimulate more research efforts on theory development and empirical studies on HRM practices of venture firms. Also, it is hoped that government conduct more policy studies and provide more resources in HRM area of the venture firms. Specifically, it is suggested that government take proactive steps to improve industrial skilled staff and technical researcher systems in order to alleviate the problems of workforce shortages in venture firms. And it IS also suggested that regional human resource development programs be introduced with the participation of the firms, local governments, and universities.