• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.1
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• pp.141-158
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• 2023

From the perspective of an entrepreneur, one of the most important factors for understanding the inherent limitations of a startup, reducing the risk of failure, and succeeding is the composition of the talent, that is, the founding team. Therefore, a common concern experienced by entrepreneurs in the pre-entrepreneurship stage or the early stage of startup is the choice between independent startups and co-founding start-up. Nonetheless, in Korea, the share of independent entrepreneurship is significantly higher than that of co-founding start-up. On the other hand, focusing on the fact that many successful global innovative companies are in the form of co-founding start-up, the success factors of co-founding start-up were examined. Most of the related preceding studies are studies that identify the capabilities and characteristics of individual entrepreneurs as factors influencing the survival and success of entrepreneurship, and there is a lack of research on partnerships, that is, co-founding start-up, which are common in the field of entrepreneurship ecosystems. Therefore, this study attempted a multi-case study through in-depth interviews, collection of relevant data, analysis of contextual information, and consideration of previous studies targeting co-founders of domestic startups that succeeded in opportunistic startups. Through this, a model for deriving the phased characteristics and key success factors of co-founding start-up was proposed. As a result of the study, the key element of the preliminary start-up stage was 'opportunity', and the success factors were 'opportunity recognition through entrepreneur's experience' and 'idea development'. The key element in the early stages of start-up is "start-up team," and the success factor is "trust and complement of start-up team," and synergy is shown when "diversity and homogeneity of start-up team" are harmonized. In addition, conflicts between co-founders may occur in the early stages of start-ups, which has a large impact on the survival of start-ups. The conflict between the start-up team could be overcome through constant "mutual understanding and respect through communication" and "clear division of work and role sharing." It was confirmed that the core element of the start-up growth stage was 'resources', and 'securing excellent talent' and 'raising external funds' were important factors for success. These results are expected to overcome the limitations of start-up companies, such as limited resources, lack of experience, and risk of failure, in entrepreneurship studies, and prospective entrepreneurs preparing for a start-up in a situation where the form of co-founding start-up is attracting attention as one of the alternatives to increase the success rate. It has implications for various stakeholders in the entrepreneurial ecosystem.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.5
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• pp.17-30
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• 2022

It has been verified in several studies that dynamic capabilities has a very important effect on the competitive advantage of technology startups. And the network has an important influence on this dynamic capability. This is even more important for start-ups that lack the resources and knowledge. Networks that directly and significantly affect dynamic capabilities have been studied mainly the value chain internal. However, network activities of start-ups are conducted not only with the value chain internal networks but also with the value chain external networks. Therefore, it is necessary to study the effect of the value chain internal and external network activity of start-ups on the dynamic capabilities, but prior studies are lacked. In this study, We make a model that encompass the value chain internal and external network for technology startups, and a study was conducted to demonstrate the effect on dynamic capabilities and competitive advantage. As a result of the study, value chain internal network activity directly and significantly affected dynamic capabilities, and value chain external network activity did not directly significantly affect dynamic capacity. And dynamic capabilities had a significant effect on competitive advantage. As confirmed through additional research, value chain external network activity affects value chain internal network activity, and through this, dynamic capabilities are strengthened, and positively affect competitive advantage.. The intensity of value chain external network activity was not significant to dynamic capabilities and the diversity of value chain external network activity had a significant effect on the competitive advantage by double mediating the value chain internal network activity and dynamic capability. Through this study, it is confirmed that the value chain internal networks is important in order for startups to strengthen their dynamic capabilities and increase their competitive advantage, and that both strong and diversified the value chain internal networks positively affects competitive advantage by enhancing dynamic capabilities.

• Korean Journal of Environment and Ecology
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• v.37 no.6
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• pp.429-438
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• 2023

The forest in the lower Tumen River serves as an important ecosystem spanning the territories of North Korea, Russia, and China, and it provides habitat and movement corridors for diverse mammals, including the endangered Amur tiger (Panthera tigris) and Amur leopard (Panthera pardus). This study focuses on the Mijiang area, situated as a potential ecological corridor connecting North Korea and China in the lower Tumen River, playing a crucial role in conserving and restoring the biodiversity of the Korean Peninsula. This study aimed to identify mammal species and estimate their relative abundance, occupancy, and distribution based on the 48 camera traps installed in the Mijiang area from May 2019 to May 2021. The results confirmed the presence of 18 mammal species in the Mijiang area, including large carnivores like tigers and leopards. Among the dominant mammals, four species of ungulates showed high occupancy and detection rates, particularly the Roe deer (Capreolus pygargus) and Wild boar (Sus scrofa). The roe deer was distributed across all areas with a predicted high occupancy rate of 0.97, influenced by altitude, urban residential areas, and patch density. Wild boars showed a predicted occupancy rate of 0.73 and were distributed throughout the entire area, with factors such as wetland ratio, grazing intensity, and spatial heterogeneity in aspects of the landscape influencing their occupancy and detection rates. Sika deer (Cervus nippon) exhibited a predicted occupancy rate of 0.48, confined to specific areas, influenced by slope, habitat fragmentation diversity affecting detection rates, and the ratio of open forests impacting occupancy. Water deer (Hydropotes inermis) displayed a very low occupancy rate of 0.06 along the Tumen River Basin, with higher occupancy in lower altitude areas and increased detection in locations with high spatial heterogeneity in aspects. This study confirmed that the Mijiang area serves as a habitat supporting diverse mammals in the lower Tumen River while also playing a crucial role in facilitating animal movement and habitat connectivity. Additionally, the occupancy prediction model developed in this study is expected to contribute to predicting mammal distribution within the disrupted Tumen River basin due to human interference and identifying and protecting potential ecological corridors in this transboundary region.

• Journal of Christian Education in Korea
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• v.78
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• pp.57-85
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• 2024

Purpose of the study: In order to utilize artificial intelligence-generated AI in educational ministry, this study analyzes the concept of artificial intelligence and generative AI and the educational theological aspects of educational ministry to find ways to efficiently utilize artificial intelligence ChatGPT in educational ministry. Contents and methods of the study: The contents of this study are. First, the contents of this study were analyzed by dividing the concepts of artificial intelligence and generative AI into the concept of artificial intelligence, types of artificial intelligence, and generative language model AI ChatGPT. Second, the educational theological analysis of educational ministry was divided into the concept of educational ministry, the goals of educational ministry, the content of educational ministry, and the direction of educational ministry in the era of artificial intelligence. Third, the plan to use artificial intelligence ChatGPT in educational ministry is to provide tools for writing sermon manuscripts, preparation tools for worship and prayer, and church education, focusing on the five functions of the early church community. It was analyzed by dividing it into tools for teaching, tools for teaching materials for believers, and tools for serving and volunteering. Conclusion and Recommendation: The conclusion of this study is that, first, when writing sermon manuscripts through artificial intelligence ChatGPT, high-quality sermon manuscripts can be written through the preacher's spirituality, faith, and insight. Second, through artificial intelligence ChatGPT, you can efficiently design and plan worship services and prepare services that serve the congregation objectively through various scenarios. Third, by using artificial intelligence ChatGPT in church education, it can be used while maintaining a complementary relationship with teachers through collaboration with human and artificial intelligence teachers. Fourth, through artificial intelligence ChatGPT, we provide a program that allows members of the church community to share spiritual fellowship, a plan to meet the needs of church members and strengthen interdependence, and an attitude of actively welcoming new people and respecting diversity. It provides useful materials that can play an important role in giving, loving, serving, and growing together in the love of Christ. Lastly, through artificial intelligence ChatGPT, we are seeking ways to provide various information about volunteer activities, learning support for children and youth in the community, mentoring-related programs, and playing a leading role in forming a village community in the local community.

• Asia pacific journal of information systems
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• v.17 no.4
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• pp.31-59
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• 2007

We frequently use search engines to find relevant information in the Web but still end up with too much information. In order to solve this problem of information overload, ranking algorithms have been applied to various domains. As more information will be available in the future, effectively and efficiently ranking search results will become more critical. In this paper, we propose a ranking algorithm for the Semantic Web resources, specifically RDF resources. Traditionally, the importance of a particular Web page is estimated based on the number of key words found in the page, which is subject to manipulation. In contrast, link analysis methods such as Google's PageRank capitalize on the information which is inherent in the link structure of the Web graph. PageRank considers a certain page highly important if it is referred to by many other pages. The degree of the importance also increases if the importance of the referring pages is high. Kleinberg's algorithm is another link-structure based ranking algorithm for Web pages. Unlike PageRank, Kleinberg's algorithm utilizes two kinds of scores: the authority score and the hub score. If a page has a high authority score, it is an authority on a given topic and many pages refer to it. A page with a high hub score links to many authoritative pages. As mentioned above, the link-structure based ranking method has been playing an essential role in World Wide Web(WWW), and nowadays, many people recognize the effectiveness and efficiency of it. On the other hand, as Resource Description Framework(RDF) data model forms the foundation of the Semantic Web, any information in the Semantic Web can be expressed with RDF graph, making the ranking algorithm for RDF knowledge bases greatly important. The RDF graph consists of nodes and directional links similar to the Web graph. As a result, the link-structure based ranking method seems to be highly applicable to ranking the Semantic Web resources. However, the information space of the Semantic Web is more complex than that of WWW. For instance, WWW can be considered as one huge class, i.e., a collection of Web pages, which has only a recursive property, i.e., a 'refers to' property corresponding to the hyperlinks. However, the Semantic Web encompasses various kinds of classes and properties, and consequently, ranking methods used in WWW should be modified to reflect the complexity of the information space in the Semantic Web. Previous research addressed the ranking problem of query results retrieved from RDF knowledge bases. Mukherjea and Bamba modified Kleinberg's algorithm in order to apply their algorithm to rank the Semantic Web resources. They defined the objectivity score and the subjectivity score of a resource, which correspond to the authority score and the hub score of Kleinberg's, respectively. They concentrated on the diversity of properties and introduced property weights to control the influence of a resource on another resource depending on the characteristic of the property linking the two resources. A node with a high objectivity score becomes the object of many RDF triples, and a node with a high subjectivity score becomes the subject of many RDF triples. They developed several kinds of Semantic Web systems in order to validate their technique and showed some experimental results verifying the applicability of their method to the Semantic Web. Despite their efforts, however, there remained some limitations which they reported in their paper. First, their algorithm is useful only when a Semantic Web system represents most of the knowledge pertaining to a certain domain. In other words, the ratio of links to nodes should be high, or overall resources should be described in detail, to a certain degree for their algorithm to properly work. Second, a Tightly-Knit Community(TKC) effect, the phenomenon that pages which are less important but yet densely connected have higher scores than the ones that are more important but sparsely connected, remains as problematic. Third, a resource may have a high score, not because it is actually important, but simply because it is very common and as a consequence it has many links pointing to it. In this paper, we examine such ranking problems from a novel perspective and propose a new algorithm which can solve the problems under the previous studies. Our proposed method is based on a class-oriented approach. In contrast to the predicate-oriented approach entertained by the previous research, a user, under our approach, determines the weights of a property by comparing its relative significance to the other properties when evaluating the importance of resources in a specific class. This approach stems from the idea that most queries are supposed to find resources belonging to the same class in the Semantic Web, which consists of many heterogeneous classes in RDF Schema. This approach closely reflects the way that people, in the real world, evaluate something, and will turn out to be superior to the predicate-oriented approach for the Semantic Web. Our proposed algorithm can resolve the TKC(Tightly Knit Community) effect, and further can shed lights on other limitations posed by the previous research. In addition, we propose two ways to incorporate data-type properties which have not been employed even in the case when they have some significance on the resource importance. We designed an experiment to show the effectiveness of our proposed algorithm and the validity of ranking results, which was not tried ever in previous research. We also conducted a comprehensive mathematical analysis, which was overlooked in previous research. The mathematical analysis enabled us to simplify the calculation procedure. Finally, we summarize our experimental results and discuss further research issues.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.177-193
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• 2014

Over the past decade, there has been a rapid diffusion of electronic commerce and a rising number of interconnected networks, resulting in an escalation of security threats and privacy concerns. Electronic commerce has a built-in trade-off between the necessity of providing at least some personal information to consummate an online transaction, and the risk of negative consequences from providing such information. More recently, the frequent disclosure of private information has raised concerns about privacy and its impacts. This has motivated researchers in various fields to explore information privacy issues to address these concerns. Accordingly, the necessity for information privacy policies and technologies for collecting and storing data, and information privacy research in various fields such as medicine, computer science, business, and statistics has increased. The occurrence of various information security accidents have made finding experts in the information security field an important issue. Objective measures for finding such experts are required, as it is currently rather subjective. Based on social network analysis, this paper focused on a framework to evaluate the process of finding experts in the information security field. We collected data from the National Discovery for Science Leaders (NDSL) database, initially collecting about 2000 papers covering the period between 2005 and 2013. Outliers and the data of irrelevant papers were dropped, leaving 784 papers to test the suggested hypotheses. The co-authorship network data for co-author relationship, publisher, affiliation, and so on were analyzed using social network measures including centrality and structural hole. The results of our model estimation are as follows. With the exception of Hypothesis 3, which deals with the relationship between eigenvector centrality and performance, all of our hypotheses were supported. In line with our hypothesis, degree centrality (H1) was supported with its positive influence on the researchers' publishing performance (p<0.001). This finding indicates that as the degree of cooperation increased, the more the publishing performance of researchers increased. In addition, closeness centrality (H2) was also positively associated with researchers' publishing performance (p<0.001), suggesting that, as the efficiency of information acquisition increased, the more the researchers' publishing performance increased. This paper identified the difference in publishing performance among researchers. The analysis can be used to identify core experts and evaluate their performance in the information privacy research field. The co-authorship network for information privacy can aid in understanding the deep relationships among researchers. In addition, extracting characteristics of publishers and affiliations, this paper suggested an understanding of the social network measures and their potential for finding experts in the information privacy field. Social concerns about securing the objectivity of experts have increased, because experts in the information privacy field frequently participate in political consultation, and business education support and evaluation. In terms of practical implications, this research suggests an objective framework for experts in the information privacy field, and is useful for people who are in charge of managing research human resources. This study has some limitations, providing opportunities and suggestions for future research. Presenting the difference in information diffusion according to media and proximity presents difficulties for the generalization of the theory due to the small sample size. Therefore, further studies could consider an increased sample size and media diversity, the difference in information diffusion according to the media type, and information proximity could be explored in more detail. Moreover, previous network research has commonly observed a causal relationship between the independent and dependent variable (Kadushin, 2012). In this study, degree centrality as an independent variable might have causal relationship with performance as a dependent variable. However, in the case of network analysis research, network indices could be computed after the network relationship is created. An annual analysis could help mitigate this limitation.