• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.6
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• pp.57-70
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• 2023

The investigation delved into 457 pieces of scholarly work, encompassing articles, published theses, and dissertations from the National Research Foundation of Korea, spanning the period of the 1997 IMF financial crisis up to 2022. The materials were sourced using terms such as 'angel investment', 'angel investor', and 'angel investment attraction'. The initial phase involved filtering out redundant entries from the preliminary collection of 267 works, leaving aside pieces that didn't pertain directly to angel investment as indicated in their abstracts. The next stage of the analysis involved a more rigorous selection process. Out of 43 papers earmarked in the preceding cut, only 32 were chosen. The criteria for this focused on the exclusion of conference presentations, articles that were either not submitted or inconclusive, and those that duplicated content under different titles. The final selection of 32 papers underwent a thorough systematic literature review. These documents, all pertinent to angel investment in South Korea, were scrutinized under five distinct categories: 1) publication year, 2) themes of research, 3) strategies employed in the studies, 4) participants involved in the research, and 5) methods of research utilized. This meticulous process illuminated the existing landscape of angel investment studies within Korea. Moreover, this study pinpointed gaps in the current body of research, offering guidance on future scholarly directions and proposing social scientific theories to further enrich the field of angel investment studies and analysis also seeks to pinpoint which areas require additional exploration to energize the field of angel investment moving forward. Through a comprehensive review of literature, this research intends to validate the establishment of future research trajectories and pinpoint areas that are currently and relatively underexplored in Korea's angel investment research stream. This study revealed that current research on domestic angel investment is concentrated on several areas: 1) the traits of angel investors, 2) the motivations behind angel investing, 3) startup ventures, 4) relevant institutions and policies, and 5) the various forms of angel investments. It was determined that there is a need to broaden the scope of research to aid in enhancing and stimulating the scale of domestic angel investing. This includes research into performance analysis of angel investments and detailed case studies in the field. Furthermore, the study emphasizes the importance of diversifying research efforts. Instead of solely focusing on specific factors like investment types, startups, accelerators, venture capital, and regulatory frameworks, there is a call for research that explores a variety of associated variables. These include aspects related to crowdfunding and return on investment in the context of angel investing, ensuring a more holistic approach to research in this domain. Specifically, there's a clear need for more detailed studies focusing on the relationships with variables that serve as dependent variables influencing the outcomes of angel investments. Moreover, it's essential to invigorate both qualitative and quantitative research that delves into the theoretical framework from multiple perspectives. This involves analyzing the structure of variables that have an impact on angel investments and the decisions surrounding these investments, thereby enriching the theoretical foundation of this field. Finally, we presented the direction of development for future research by confirming that the effect on the completeness of the business plan is high or low depending on the satisfaction of the entrepreneurs in addition to the components.

• Information Systems Review
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• v.18 no.3
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• pp.209-233
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• 2016

Internet companies that utilize social network have increased in number. The introduction of diverse social media services facilitated innovative changes in e-business. Social network service (SNS), which is a domain of social media, is a web-based service designed to strengthen human relations in the Internet and build new social relations. The remarkable growth of social network services and the profit generation and perception of this service are the new growth engines of this digital age. Given this development, many global IT companies views SNS as the most powerful form of social media. Thus, they invest efforts to develop business models using SNS.2) This study verifies the impact of privacy exposure in SNS as a result of privacy invasion. This study examines the purpose of using the SNS and user's awareness of the significance of personal information, which are key factors that affect self-disclosure of personal information. This study utilizes theory of reasoned action (TRA) to provide a theoretical platform that describes the specific behavior and emotional response of individuals. This study presents a research model that considers negative attitude (negatude). In this model, self-disclosure in SNS is considered a TRA. TRA is a subjective norm, a behavioral intention, and a key variable of exposure behavior. A survey was conducted on college students at Y university in Seoul to empirically verify the research model. The students have experiences in using SNS. A total of 198 samples were collected. Path analysis was applied to analyze the relations of factors. The results of path analysis show the statistically insignificant impact of privacy invasion on negatude, subjective norm, behavioral intention, and exposure behavior. The impact of unrecognized privacy invasion was also considered insignificant. The impacts of intention to use SNS on negatude, subjective norm, behavioral intention, and exposure behavior was significant. A significant impact was also found for the significance of personal information on subjective norm, behavioral intention, and exposure behavior, whereas the impact on negatude was insignificant. The impact of subjective norm on behavioral intention was significant. Lastly, the impact of behavioral intention on exposure behavior was insignificant. These findings are significant because the study examined the process of self-disclosure by integrating psychological and social factors based on theoretical discussion.

• Journal of Korean Society of Forest Science
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• v.113 no.1
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• pp.118-130
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• 2024

Rehabilitation of mining areas can reduce damage to ecosystems. However, the effects of rehabilitation on ecosystem services (ESs) and its contribution to local communities are not well known. Thus, the aims of this study were to clearly identify the ES beneficiaries affected by mining activities, to determine how the beneficiaries profit from surrounding areas in cooperation with local stakeholders, and to manage the rehabilitation areas for the ESs that the beneficiaries want. This study chose 18 ESs (4 provisioning, 7 regulating, 5 cultural, and 2 habitat services) based on The Economics of Ecosystems and Biodiversity. A semi-structured questionnaire survey using an 11-point Likert scale was conducted among 87 community residents to investigate social awareness and identify key ESs. The survey results from two local communities showed high awareness and demands mainly on cultural (mental and physical health, aesthetic appreciation, and recreation) and regulating services (local climate and air quality, and moderation of extreme events). These services were related to the daily lives of residents in local communities, provided positive benefits, and potentially improved the residents' future livelihoods. However, the average questionnaire scores were limited to 6-7 points, indicating that the benefits to local communities were meager. The residents' awareness of provisioning service was negative, even if it provided goods and profit opportunities. This indicated a disconnection between local communities and provisioning services due to forest rehabilitation that did not consider local communities that traditionally relied on specific provisioning services before the onset of mining activities. Future forest rehabilitation in abandoned mine areas must consider the welfare of local communities for sustainable use of rehabilitated forests and enhancing ESs. In this study, only a qualitative evaluation based on frequency analyses was conducted. The quantification and valuation of key ESs are warranted in the future to promote ESs from forest rehabilitation in abandoned mine areas. The study results would be useful for developing site-specific ES promotion strategies for reforesting mine areas.

• Journal of Venture Innovation
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• v.7 no.1
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• pp.1-15
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• 2024

This study aims to elucidate the characteristics of big tech platform companies' sports content business in an environment of rapid digital transformation. Specifically, this study examines the market structure of big tech platform companies with a focus on Amazon, revealing the role of sports content within this structure through an analysis of Amazon's sports marketing business and provides an outlook on the sports content business of big tech platform companies. Based on two-sided market platform business models, big tech platform companies incorporate sports content as a strategy to enhance the value of their platforms. Therefore, sports content is used as a tool to enhance the value of their platforms and to consolidate their monopoly position by maximizing profits by increasing the synergy of platform ecosystems such as infrastructure. Amazon acquires popular live sports broadcasting rights on a continental or national basis and supplies them to its platforms, which not only increases the number of new customers and purchasing effects, but also provides IT solution services to sports organizations and teams while planning and supplying various promotional contents, thus creates synergy across Amazon's platforms including its advertising business. Amazon also expands its business opportunities and increases its overall value by supplying live sports contents to Amazon Prime Video and Amazon Prime, providing technical services to various stakeholders through Amazon Web Services, and offering Amazon Marketing Cloud services for analyzing and predicting advertisers' advertising and marketing performance. This gives rise to a new paradigm in the sports marketing business in the digital era, stemming from the difference in market structure between big tech companies based on two-sided market platforms and legacy global companies based on one-sided markets. The core of this new model is a business through the development of various contents based on live sports streaming rights, and sports content marketing will become a major field of sports marketing along with traditional broadcasting rights and sponsorship. Big tech platform global companies such as Amazon, Apple, and Google have the potential to become new global sports marketing companies, and the current sports marketing and advertising companies, as well as teams and leagues, are facing both crises and opportunities.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.3
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• pp.139-153
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• 2024

The importance of sustainable growth and cost reduction has increased globally, leading to the expansion of outsourcing by companies. Additionally, the spread of the platform economy has brought changes in the way we work, and the online outsourcing market, where tasks are mediated through platforms, is growing. Academically, while research on general outsourcing is actively conducted, studies on online outsourcing are relatively insufficient compared to its actual utilization. This study aims to analyze the factors and performance factors of online outsourcing utilization by startups, to identify the effects and concerns of using online outsourcing from multiple perspectives, and to suggest the roles of various stakeholders for effective utilization and industry development. For the research, a survey was conducted with 281 employees of startups who have experience in using online outsourcing, and the main findings are as follows. First, the enhancement of efficiency, profitability, and innovation through the use of online outsourcing positively affects organizational commitment and job satisfaction of startup members. Especially, the improvement of efficiency due to the use of online outsourcing has a significant effect on enhancing job satisfaction. Second, concerns about the burden of online outsourcing fees or uncertain outcomes negatively affect organizational commitment and job satisfaction. Third, there are perceptual differences in the motivations and performance regarding the utilization of online outsourcing depending on the job position. Practitioners perceive that the use of online outsourcing increases organizational commitment, whereas managers have relatively higher concerns about the uncertainty of outsourced task outcomes and information security. Through this study, the possibility that human resource shortages and employee management issues in startups can be improved through online outsourcing was confirmed. By verifying the influence of various factors of online outsourcing utilization, this study also provides meaningful implications for establishing business strategies for online outsourcing intermediary platform companies and for formulating startup support policies by government and other startup support organizations.

• Journal of Korea Port Economic Association
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• v.40 no.1
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• pp.151-163
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• 2024

This study sought to confirm the impact of analytical methods and behavioral economic theory factors on decision-making when making chartering decisions in the dry bulk shipping market. This study on chartering decision-making model was began to verify why shipping companies do not make rational decision-making and behavior based on analytical methods such as freight prediction and process of alternative selection in the same market situation. To understand the chartering decision-making model, it is necessary to study the impact of behavioral economic theory such as heuristics, loss aversion, and herding behavior on chartering decision-making. Through AHP analysis, the importance of the method factors relied upon in chartering decision-making. The dependence of the top factors in chartering decision-making was in the following order: market factors, heuristics, internal factors, herding behavior, and loss aversion. Market factors, heuristics, and internal factors. As for detailed factors, spot freight index and empirical intuition were confirmed as the most important factors relied on when making decisions. It was confirmed that empirical intuition is more important than internal analysis, which is an analytical method. This study can be said to be meaningful in that it academically researched and proved the bounded rationality of humans, which cannot be fully rational, and sometimes relies on experience or psychological tendencies, by applying it to the chartering decision-making model in the dry bulk shipping market. It also suggests that in the dry bulk shipping market, which is uncertain and has a high risk of loss due to decision-making, the experience and insight of decision makers have a very important impact on the performance and business profits of the operation part of shipping companies. Even though chartering are a decision-making field that requires judgment and intuition based on heuristics, decision-makers need to be aware of this decision-making model in order to reduce repeated mistakes of deciding contrary to market situation. It also suggests that there is a need to internally research analytical methods and procedures that can complement heuristics such as empirical intuition.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.111-131
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• 2015

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.4
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• pp.193-216
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• 2020

Due to the recent mid- to long-term slump and falling growth rates in the global economy, interest in organizational structures that create new products or services as a new alternative to survive and develop in an opaque environment both internally and externally, and enhance organizational sustainability through changes in production methods and business innovation is increasing day by day. In this atmosphere, we agree that the growth of small and medium-sized venture companies has a significant impact on the national economy, and various efforts are being made to enhance the technological innovation capabilities of the members so that these small and medium-sized venture companies can enhance and sustain their performance. The purpose of this study is also to investigate how the technological innovation capabilities of small and medium-sized venture companies correlate with the performance of knowledge management and to analyze the role of network capabilities to organize the strategic activities of enterprise to obtain the resources and organizational capabilities to be used for value creation from external networks. In other words, research was conducted on the impact of technological innovation capabilities of small and medium venture companies on knowledge management performance by using network capabilities as parameters. Therefore, in this study, we would like to verify the hypothesis that innovation capabilities will have a positive impact on knowledge management performance by using network capabilities of small and medium venture companies. Economic activities based on technological innovation capabilities should respond quickly to new changes in an environment where uncertainty has increased, and lead to macro-economic growth and development as well as overcoming long-term economic downturns so that they can become the nation's new growth engine as well as sustainable growth and survival of the organization. In addition, this study was conducted by setting the most important knowledge management performance within the organization as a dependent variable. As a result, R&D and learning capabilities among technological innovation capabilities have no impact on financial performance. In contrast, it was shown that corporate innovation activities have a positive impact on both financial and non-financial performance. The fact that non-financial factors such as quality and productivity improvement are identified in the management of small and medium-sized venture companies utilizing their technological innovation capabilities is contrary to a number of studies by those corporate innovation activities affect financial performance during prior research. The reason for this result is that research companies have been out of start-up companies for more than seven years, but sales are less than 10 billion won, and unlike start-up companies, R&D and learning capabilities have more positive effects on intangible non-financial performance than financial performance. Corporate innovation activities have been shown to have a positive (+) impact on both financial and non-financial performance, while R&D and learning capabilities have a positive (+) impact on financial performance by parameters of network capability. Corporate innovation activities have been shown to have no impact on both financial and non-financial performance, and R&D and learning capabilities have no impact on non-financial performance. It could be seen that the parameter effects of network competency are limited to when R&D and learning competencies are derived from quantitative financial performance. It could be seen that the parameter effects of network competency are limited to when R&D and learning competencies are derived from quantitative financial performance.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.93-112
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• 2015

Depending on the change in consumer's consumption pattern, existing retail shop has evolved in hypermarket or convenience store offering grocery and daily products mostly. Therefore, it is important to maintain the inventory levels and proper product configuration for effectively utilize the limited space in the retail store and increasing sales. Accordingly, this study proposed proper product configuration and inventory level strategy based on RFM(Recency, Frequency, Monetary) model and SOM(self-organizing map) for manage the retail shop effectively. RFM model is analytic model to analyze customer behaviors based on the past customer's buying activities. And it can differentiates important customers from large data by three variables. R represents recency, which refers to the last purchase of commodities. The latest consuming customer has bigger R. F represents frequency, which refers to the number of transactions in a particular period and M represents monetary, which refers to consumption money amount in a particular period. Thus, RFM method has been known to be a very effective model for customer segmentation. In this study, using a normalized value of the RFM variables, SOM cluster analysis was performed. SOM is regarded as one of the most distinguished artificial neural network models in the unsupervised learning tool space. It is a popular tool for clustering and visualization of high dimensional data in such a way that similar items are grouped spatially close to one another. In particular, it has been successfully applied in various technical fields for finding patterns. In our research, the procedure tries to find sales patterns by analyzing product sales records with Recency, Frequency and Monetary values. And to suggest a business strategy, we conduct the decision tree based on SOM results. To validate the proposed procedure in this study, we adopted the M-mart data collected between 2014.01.01~2014.12.31. Each product get the value of R, F, M, and they are clustered by 9 using SOM. And we also performed three tests using the weekday data, weekend data, whole data in order to analyze the sales pattern change. In order to propose the strategy of each cluster, we examine the criteria of product clustering. The clusters through the SOM can be explained by the characteristics of these clusters of decision trees. As a result, we can suggest the inventory management strategy of each 9 clusters through the suggested procedures of the study. The highest of all three value(R, F, M) cluster's products need to have high level of the inventory as well as to be disposed in a place where it can be increasing customer's path. In contrast, the lowest of all three value(R, F, M) cluster's products need to have low level of inventory as well as to be disposed in a place where visibility is low. The highest R value cluster's products is usually new releases products, and need to be placed on the front of the store. And, manager should decrease inventory levels gradually in the highest F value cluster's products purchased in the past. Because, we assume that cluster has lower R value and the M value than the average value of good. And it can be deduced that product are sold poorly in recent days and total sales also will be lower than the frequency. The procedure presented in this study is expected to contribute to raising the profitability of the retail store. The paper is organized as follows. The second chapter briefly reviews the literature related to this study. The third chapter suggests procedures for research proposals, and the fourth chapter applied suggested procedure using the actual product sales data. Finally, the fifth chapter described the conclusion of the study and further research.