• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.19 no.2
• /
• pp.1-11
• /
• 2024

In the midst of the current turbulent global economy, traditional investment metrics are undergoing a metamorphosis, signaling the onset of what's often referred to as an "Investment cold season". Early-stage startups, despite their boundless potential, grapple with immediate revenue constraints, intensifying their pursuit of critical investments. While financial indicators once took center stage in investment evaluations, a notable paradigm shift is underway. Organizational culture, once relegated to the sidelines, has now emerged as a linchpin in forecasting a startup's resilience and enduring trajectory. Our comprehensive research, integrating insights from CVF and OCAI, unveils the intricate relationship between organizational culture and its magnetic appeal to investors. The results indicate that startups with a pronounced external focus, expertly balanced with flexibility and stability, hold particular allure for investment consideration. Furthermore, the study underscores the pivotal role of adhocracy and market-driven mindsets in shaping investment desirability. A significant observation emerges from the study: startups, whether they secured investment or failed to do so, consistently display strong clan culture, highlighting the widespread importance of nurturing a positive employee environment. Leadership deeply anchored in market culture, combined with an unwavering commitment to innovation and harmonious organizational practices, emerges as a potent recipe for attracting investor attention. Our model, with an impressive 88.3% predictive accuracy, serves as a guiding light for startups and astute investors, illuminating the intricate interplay of culture and investment success in today's economic landscape.

• Industry Promotion Research
• /
• v.9 no.2
• /
• pp.29-35
• /
• 2024

Recently, the Korean financial market has implemented a ban on unleveraged short selling, and leveraged short selling, which involves selling borrowed securities, is called general short selling. This study sought to come up with improvement measures to revitalize short selling from the perspective of individual investors. Short selling refers to selling stocks you do not own in the stock market, predicting that the stock price of the stock will fall, and borrowing stocks to sell them. Based on the results of this study, the short selling market's growth and improvement plans are as follows. First, a plan must be developed to expand short selling opportunities for individual investors. In the domestic short selling market, including KOSPI and KOSDAQ, foreign and institutional participants account for more than 95% of the market, and individual investors are very small. Therefore, its expansion is inevitable. Second, monitoring and punishment for unfair short selling transactions must be strengthened. Representative improvement measures that can minimize the side effects of short selling include strengthening monitoring of unfair trading and short selling, and raising the level of punishment. In addition, measures must be taken to further increase the level of punishment for short selling related to unfair transactions. Third, the short selling reporting and disclosure system needs to be improved. In the case of Korea, short selling transactions are not yet as active as in developed countries, but there is a need to expand the disclosure system to strengthen market transparency in preparation for future short selling transactions becoming more active. In conclusion, it is reported that if short selling regulations are excessively strengthened, losses may occur in terms of price efficiency and market liquidity, which may ultimately have a negative impact on the market. Therefore, policies related to short selling must be made while taking into account the positive aspects of regulatory effects and the negative impact on the market.

• Journal of Intelligence and Information Systems
• /
• v.23 no.2
• /
• pp.139-160
• /
• 2017

Technology commercialization creates effective economic value by linking the company's R & D processes and outputs to the market. This technology commercialization is important in that a company can retain and maintain a sustained competitive advantage. In order for a specific technology to be commercialized, it goes through the stage of technical planning, technology research and development, and commercialization. This process involves a lot of time and money. Therefore, the duration and cost of technology commercialization are important decision information for determining the market entry strategy. In addition, it is more important information for a technology investor to rationally evaluate the technology value. In this way, it is very important to scientifically estimate the duration and cost of the technology commercialization. However, research on technology commercialization is insufficient and related methodology are lacking. In this study, we propose an evaluation model that can estimate the duration and cost of R & D technology commercialization for small and medium-sized enterprises. To accomplish this, this study collected the public data of the National Science & Technology Information Service (NTIS) and the survey data provided by the Small and Medium Business Administration. Also this study will develop the estimation model of commercialization duration and cost of R&D performance on using these data based on the market approach, one of the technology valuation methods. Specifically, this study defined the process of commercialization as consisting of development planning, development progress, and commercialization. We collected the data from the NTIS database and the survey of SMEs technical statistics of the Small and Medium Business Administration. We derived the key variables such as stage-wise R&D costs and duration, the factors of the technology itself, the factors of the technology development, and the environmental factors. At first, given data, we estimates the costs and duration in each technology readiness level (basic research, applied research, development research, prototype production, commercialization), for each industry classification. Then, we developed and verified the research model of each industry classification. The results of this study can be summarized as follows. Firstly, it is reflected in the technology valuation model and can be used to estimate the objective economic value of technology. The duration and the cost from the technology development stage to the commercialization stage is a critical factor that has a great influence on the amount of money to discount the future sales from the technology. The results of this study can contribute to more reliable technology valuation because it estimates the commercialization duration and cost scientifically based on past data. Secondly, we have verified models of various fields such as statistical model and data mining model. The statistical model helps us to find the important factors to estimate the duration and cost of technology Commercialization, and the data mining model gives us the rules or algorithms to be applied to an advanced technology valuation system. Finally, this study reaffirms the importance of commercialization costs and durations, which has not been actively studied in previous studies. The results confirm the significant factors to affect the commercialization costs and duration, furthermore the factors are different depending on industry classification. Practically, the results of this study can be reflected in the technology valuation system, which can be provided by national research institutes and R & D staff to provide sophisticated technology valuation. The relevant logic or algorithm of the research result can be implemented independently so that it can be directly reflected in the system, so researchers can use it practically immediately. In conclusion, the results of this study can be a great contribution not only to the theoretical contributions but also to the practical ones.

• Journal of Venture Innovation
• /
• v.1 no.1
• /
• pp.49-65
• /
• 2018

For a long time the German economy was primarily defined by large corporations and thriving small and medium-sized enterprises. Since about 2005 a second strand has started to emerge and it is one which is becoming increasingly important and is creating jobs - start-ups in the digital sector. This start-up activity is taking an important role in Germany's economic development: Start-up companies spawn innovations and create jobs, thus promoting the concept of competition. In general "start-up" refers to digitally-driven companies that are not more than five years old. Germany's start-up policy consists of three main parts. First of all, Germany has the characteristics of technology-based start-ups. The Hartz reform since 2002 has shown its focus on technology-based start-ups. In particular, it is the most appropriate for a start-up company to take the role of a new technology company to respond to changes in the global industrial structure. Second, it is approaching from a long-term perspective. In this regard, the small business policy, including Germany's new business policy, is seen as a tradition that can be consistent and can make policy decisions based on the basics rather than following the times. Third, the government is implementing policies centered on demand. Germany's start-up policy is summarized as a technology-based policy and new job creation. The policy response is that the government seeks the best combination of policies by adapting them to the times from the broad trend of employment market policies. What is important here is that policies are made based on consumers, not suppliers, in the process of policy making and implementation. With the Digital Agenda 2020 the Federal government has likewise committed itself to preparing the digital economy for international competition and making Germany the "No. 1 digital growth country in Europe". Ever since 1998 the Federal Ministry for Economic Affairs and Energy (BMWi) has awarded the "EXIST" start-up scholarship to students and graduates. The Ministry also invests in the High Tech start-up fund. Together with Kreditanstalt für Wiederaufbau (KfW) and 18 other investors from the world of business the seed investor promotes young technology companies. Germany offers start-ups a good infrastructure and lots of funding opportunities. Berlin is regarded as Europe's start-up capital and also attracts lots of international young entrepreneurs.

• The KIPS Transactions:PartD
• /
• v.12D no.3 s.99
• /
• pp.417-428
• /
• 2005

Today, IT organizations perform projects with vision related to marketing and financial profit. The objective of realizing the vision is to improve the project performing ability in terms of QCD. Organizations have made a lot of efforts to achieve this objective through process improvement. Large companies such as IBM, Ford, and GE have made over $80\%$ of success through business process re-engineering using information technology instead of business improvement effect by computers. It is important to collect, analyze and manage the data on performed projects to achieve the objective, but quantitative measurement is difficult as software is invisible and the effect and efficiency caused by process change are not visibly identified. Therefore, it is not easy to extract the strategy of improvement. This paper measures and analyzes the project performance, focusing on organizations' external effectiveness and internal efficiency (Qualify, Delivery, Cycle time, and Waste). Based on the measured project performance scores, an OT (Opportunity Tree) model was designed for optimizing the project performance. The process of design is as follows. First, meta data are derived from projects and analyzed by quantitative GQM(Goal-Question-Metric) questionnaire. Then, the project performance model is designed with the data obtained from the quantitative GQM questionnaire and organization's performance score for each area is calculated. The value is revised by integrating the measured scores by area vision weights from all stakeholders (CEO, middle-class managers, developer, investor, and custom). Through this, routes for improvement are presented and an optimized improvement method is suggested. Existing methods to improve software process have been highly effective in division of processes' but somewhat unsatisfactory in structural function to develop and systemically manage strategies by applying the processes to Projects. The proposed OT model provides a solution to this problem. The OT model is useful to provide an optimal improvement method in line with organization's goals and can reduce risks which may occur in the course of improving process if it is applied with proposed methods. In addition, satisfaction about the improvement strategy can be improved by obtaining input about vision weight from all stakeholders through the qualitative questionnaire and by reflecting it to the calculation. The OT is also useful to optimize the expansion of market and financial performance by controlling the ability of Quality, Delivery, Cycle time, and Waste.

• Journal of Korean Society of Transportation
• /
• v.35 no.4
• /
• pp.360-373
• /
• 2017

The BTO-a projects is the types, which has a demand risk among the type of PPP projects in Korea. When demand risk is realized, private investor encounters financial difficulties due to lower revenue than its expectation and the government may also have a problem in stable infrastructure operation. In this regards, the government has applied various risk sharing policies in response to demand risk. However, the amount of government's risk sharing is the government's contingent liabilities as a result of demand uncertainty, and it fails to be quantified by the conventional NPV method of expressing in the text of the concession agreement. The purpose of this study is to estimate the value of investment risk sharing by the government considering the demand risk in the profit sharing system (BTO-a) introduced in 2015 as one of the demand risk sharing policy. The investment risk sharing will take the form of options in finance. Private investors have the right to claim subsidies from the government when their revenue declines, while the government has the obligation to pay subsidies under certain conditions. In this study, we have established a methodology for estimating the value of investment risk sharing by using the Black - Scholes option pricing model and examined the appropriateness of the results through case studies. As a result of the analysis, the value of investment risk sharing is estimated to be 12 billion won, which is about 4% of the investment cost of the private investment. In other words, it can be seen that the government will invest 12 billion won in financial support by sharing the investment risk. The option value when assuming the traffic volume risk as a random variable from the case studies is derived as an average of 12.2 billion won and a standard deviation of 3.67 billion won. As a result of the cumulative distribution, the option value of the 90% probability interval will be determined within the range of 6.9 to 18.8 billion won. The method proposed in this study is expected to help government and private investors understand the better risk analysis and economic value of better for investment risk sharing under the uncertainty of future demand.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.2 no.2
• /
• pp.93-120
• /
• 2007

To promote the commercialization of high technology based start-up company, it is essential to activate technology trading for the Innovative Small & Medium companies which eager to acquire technology competitiveness through technology trading and facilitate financial system which provide Small & Medium companies with technology commercialization money. This study focus on enhancing credibility of technology valuation solutions through the improvement of the existing technology valuation solutions. The existing technology valuation solutions in practice have deficiency in reasonable valuation because of subjective technology factor's calculation. And the commercialization risk of high technology based start-up companies cannot be fully reflected in the existing valuation solutions. The high risk of technology commercialization acts as a hurdle in the facilitation of providing money for the start-up companies. The improved new model will have more acceptable objectivity and reasonably reflect the commercialization risk of start-up companies. The new model has two distinctive features compared to existing solutions. The new model newly adopts commercialization success factor which reflects the risk of high technology based start-up company. And the new model excludes technology factor which is controversial among interested parties. The exclusion of technology factor will improve the objectivity of technology valuation and the adoption of commercialization success factor will solicit investor and capitalist who concern the high risk of technology based start-up companies. In conclusion, the improved new model is expected to activate technology trading and facilitate the money market through which high technology based start-up companies raise commercialization money.

• Asia pacific journal of information systems
• /
• v.19 no.2
• /
• pp.157-178
• /
• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• Knowledge Management Research
• /
• v.21 no.3
• /
• pp.45-65
• /
• 2020

It is desirable to invest in wine that increases its value, but wine investment itself is unfamiliar in Korea. Also, the process itself is unreasonable, and information is often forged, because pricing in the wine market is done by a small number of people. With the right solution, however, the wine market can be a desirable investment destination in that the longer one invests, the higher one can expect. Also, it is expected that the domestic wine consumption market will expand through the steady increase in domestic wine imports. This study presents the consortium block chain framework for revitalizing the wine market and enhancing transparency as the "right solution" of the nation's wine investment market. Blockchain governance can compensate for the shortcomings of the wine market because it guarantees desirable decision-making rights and accountability. Because the data stored in the block chain can be checked by consumers, it reduces the likelihood of counterfeit wine appearing and complements the process of unreasonably priced. In addition, digitization of assets resolves low cash liquidity and saves money and time throughout the supply chain through smart contracts, lowering entry barriers to wine investment. In particular, if the governance of the block chain is composed of 'chateau-distributor-investor' through consortium blockchains, it can create a desirable wine market. The production process is stored in the block chain to secure production costs, set a reasonable launch price, and efficiently operate the distribution system by storing the distribution process in the block chain, and forecast the amount of orders for futures trading. Finally, investors make rational decisions by viewing all of these data. The study presented a new perspective on alternative investment in that ownership can be treated like a share. We also look forward to the simplification of food import procedures and the formation of trust within the wine industry by presenting a framework for wine-owned sales. In future studies, we would like to expand the framework to study the areas to be applied.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.8 no.3
• /
• pp.125-146
• /
• 2013

Nowadays, the performance of the mobile game sales is influencing the ranking of game companies listed on KOSDAQ. In the meantime, venture capital companies had focused on online game. Recently, however, they have great interest in mobile games and mobile game companies. In addition, angel investors and accelerators are increasing investment for the mobile game companies. The most important issues for mobile game investor is how to evaluate the mobile game companies and their contents. Therefore, this study derived the evaluation factors for the mobile game company. And research method converged of the opinions of both supply side and demand side of the game industry. Ten professionals who are responsible for the supply of the game industry and CEO group & development experts of game development company were selected for survey in this study. Also ten professionals who are responsible for the demand of the game industry and the investment company were selected for survey in this study. And Delphi technique was performed according to the survey. Management skills, development capabilities, game play, feasibility, operational capabilities has emerged as five evaluation factors to evaluate the mobile game company. And the 20 sub-factors including CEO's reliability were derived. AHP(Analytic Hierarchy Process) theory is applied to analyze the importance of the qualitative elements which were derived by Delphi technique. As a result, the analysis hierarchy of evaluation factors for the mobile game company was created. Pair-wise comparison for each element was performed to analyze the importance. As a result, 'Core fun of the game' (12,2%), 'Involvement of the game' (10.3%), 'Security Reliability' (8.9%), 'Core developers' ability' (7.6%) appeared in order of importance. The significance of this study is offering more objective methodology for realistic assessment and importance of elements to evaluate mobile game company.