• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.2
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• pp.33-51
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• 2022

This study analyzes newly listed companies on KOSDAQ from 2011 to 2020 for both firms having experience in attracting venture investment before listing (VI) and those without having experience in attracting venture investment (NVI) by examining differences between two groups (VI and NVI) with respect to both the level of listing performance and that of firm performance (growth) after the listing. This paper conducts descriptive statistics, mean difference, and multiple regression analysis. Independent variables for regression models include VC investment, firm age at the time of listing, firm type, firm location, firm size, the age of VC, the level of expertise of VC, and the level of fitness of VC with investment company. Throughout this paper, results suggest that listing performance and post-listed growth are better for VI than NVI. VC investment shows a negative effect on the listing period and a positive effect on the sales growth rate. Also, the amount of VC investment has negative effects on the listing period and positive effects on the market capitalization at the time of IPO and on sales growth among growth indicators. Our evidence also implies a significantly positive effect on growth after listing for firms which belong to R&D specialized industries. In addition, it is statistically significant for several years that the firm age has a positive effect on the market capitalization growth rate. This shows that market seems to put the utmost importance on a long-term stability of management capability. Finally, among the VC characteristics such as the age of VC, the level of expertise of VC, and the level of fitness of VC with investment company, we point out that a higher market capitalization tends to be observed at the time of IPO when the level of expertise of anchor VC is high. Our paper differs from prior research in that we reexamine the venture ecosystem under the outbreak of coronavirus disease 2019 which stimulates the degradation of the business environment. In addition, we introduce more effective variables such as VC investment amount when examining the effect of firm type. It enables us to indirectly evaluate the validity of technology exception policy. Although our findings suggest that related policies such as the technology special listing system or the injection of funds into the venture ecosystem are still helpful, those related systems should be updated in a more timely fashion in order to support growth power of firms due to the rapid technological development. Furthermore, industry specialization is essential to achieve regional development, and the growth of the recovery market is also urgent.

• The Journal of Korean Society for School & Community Health Education
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• v.25 no.2
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• pp.59-70
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• 2024

Objectives: The purpose of this study is to analyze each country's infectious disease response capacities and, based on this, find areas for improvement in Korea's infectious disease management response. Methods: First, the capacity to respond to the COVID-19 infectious disease was analyzed by country using the SPAR scores of 96 countries around the world released by WHO in 2022. Second, we analyzed each country's specific COVID-19 quarantine performance using Our World in Data and the Global Health Security Index (GHSI). Results: First, the quarantine intensity index on January 24, 2021 was the highest in the Southeast Asia branch at 67.6, which had strong quarantine measures, and the lowest at 44.5 in the Africa branch. As of December 31, 2022, the quarantine intensity index in Europe was significantly lowered to 11.6. Second, the factor that influenced the SPAR indicator on the total number of patients per million population was national laboratory (C4), p=.027, and the factor that influenced the total number of deaths per million population was infection prevention and control (C9), p=.005., Risk Communication and Community Participation (C10) p=.040. The influential factor on GDP per capita was infection prevention and control (C9) p=.009, and the influential factor on GHSI was infection prevention and control (C9) p=.002. Conclusion: The research findings indicate that it was difficult to find a correlation between the SPAR, which is each country's self-assessment of their infectious disease capacities, and the number of COVID-19 cases or the intensity of pandemic responses. However, mortality rates, as well as factors such as the Global Health Security Index (GHSI) and national income, appear to be somewhat influenced. For future improvements in infectious disease management and response in our country, it is necessary to develop pandemic strategies that can reduce socio-economic costs based on more scientific and reliable data like JEE or GHSI, especially in preparation for potential unknown emerging infectious diseases. Based on this, proactive decision-making led by a control tower of experts and effective health communication are also required to respond to public health crises at a national level.

• Information Systems Review
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• v.21 no.4
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• pp.99-123
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• 2019

The software industry is a high value-added industry in the knowledge information age, and its importance is growing as it not only plays a key role in knowledge creation and utilization, but also secures global competitiveness. Among various SW available in today's business environment, Open Source Software(OSS) is rapidly expanding its activity area by not only leading software development, but also integrating with new information technology. Therefore, the purpose of this research is to empirically examine and analyze the effect of factors on the switching behavior to OSS. To accomplish the study's purpose, we suggest the research model based on "Push-Pull-Mooring" framework. This study empirically examines the two categories of antecedents for switching behavior toward OSS. The survey was conducted to employees at various firms that already switched OSS. A total of 268 responses were collected and analyzed by using the structural equational modeling. The results of this study are as follows; first, continuous maintenance cost, vender dependency, functional indifference, and SW resource inefficiency are significantly related to switch to OSS. Second, network-oriented support, testability and strategic flexibility are significantly related to switch to OSS. Finally, the results show that willingness to secures SW competitiveness has a moderating effect on the relationships between push factors and pull factor with exception of improved knowledge, and switch to OSS. The results of this study will contribute to fields related to OSS both theoretically and practically.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.171-193
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• 2018

LOD(Linked Open Data) cloud is a practical implementation of semantic web. We suggested a new method that provides identity links conveniently in LOD cloud. It also allows changes in LOD to be reflected to searching results without any omissions. LOD provides detail descriptions of entities to public in RDF triple form. RDF triple is composed of subject, predicates, and objects and presents detail description for an entity. Links in LOD cloud, named identity links, are realized by asserting entities of different RDF triples to be identical. Currently, the identity link is provided with creating a link triple explicitly in which associates its subject and object with source and target entities. Link triples are appended to LOD. With identity links, a knowledge achieves from an LOD can be expanded with different knowledge from different LODs. The goal of LOD cloud is providing opportunity of knowledge expansion to users. Appending link triples to LOD, however, has serious difficulties in discovering identity links between entities one by one notwithstanding the enormous scale of LOD. Newly added entities cannot be reflected to searching results until identity links heading for them are serialized and published to LOD cloud. Instead of creating enormous identity links, we propose LOD to prepare its own link policy. The link policy specifies a set of target LODs to link and constraints necessary to discover identity links to entities on target LODs. On searching, it becomes possible to access newly added entities and reflect them to searching results without any omissions by referencing the link policies. Link policy specifies a set of predicate pairs for discovering identity between associated entities in source and target LODs. For the link policy specification, we have suggested a set of vocabularies that conform to RDFS and OWL. Identity between entities is evaluated in accordance with a similarity of the source and the target entities' objects which have been associated with the predicates' pair in the link policy. We implemented a system "Change Acceptable In-Depth Searching System(CAIDS)". With CAIDS, user's searching request starts from depth_0 LOD, i.e. surface searching. Referencing the link policies of LODs, CAIDS proceeds in-depth searching, next LODs of next depths. To supplement identity links derived from the link policies, CAIDS uses explicit link triples as well. Following the identity links, CAIDS's in-depth searching progresses. Content of an entity obtained from depth_0 LOD expands with the contents of entities of other LODs which have been discovered to be identical to depth_0 LOD entity. Expanding content of depth_0 LOD entity without user's cognition of such other LODs is the implementation of knowledge expansion. It is the goal of LOD cloud. The more identity links in LOD cloud, the wider content expansions in LOD cloud. We have suggested a new way to create identity links abundantly and supply them to LOD cloud. Experiments on CAIDS performed against DBpedia LODs of Korea, France, Italy, Spain, and Portugal. They present that CAIDS provides appropriate expansion ratio and inclusion ratio as long as degree of similarity between source and target objects is 0.8 ~ 0.9. Expansion ratio, for each depth, depicts the ratio of the entities discovered at the depth to the entities of depth_0 LOD. For each depth, inclusion ratio illustrates the ratio of the entities discovered only with explicit links to the entities discovered only with link policies. In cases of similarity degrees with under 0.8, expansion becomes excessive and thus contents become distorted. Similarity degree of 0.8 ~ 0.9 provides appropriate amount of RDF triples searched as well. Experiments have evaluated confidence degree of contents which have been expanded in accordance with in-depth searching. Confidence degree of content is directly coupled with identity ratio of an entity, which means the degree of identity to the entity of depth_0 LOD. Identity ratio of an entity is obtained by multiplying source LOD's confidence and source entity's identity ratio. By tracing the identity links in advance, LOD's confidence is evaluated in accordance with the amount of identity links incoming to the entities in the LOD. While evaluating the identity ratio, concept of identity agreement, which means that multiple identity links head to a common entity, has been considered. With the identity agreement concept, experimental results present that identity ratio decreases as depth deepens, but rebounds as the depth deepens more. For each entity, as the number of identity links increases, identity ratio rebounds early and reaches at 1 finally. We found out that more than 8 identity links for each entity would lead users to give their confidence to the contents expanded. Link policy based in-depth searching method, we proposed, is expected to contribute to abundant identity links provisions to LOD cloud.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.1-22
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• 2021

Aspect Based Sentiment Analysis (ABSA), which analyzes sentiment based on aspects that appear in the text, is drawing attention because it can be used in various business industries. ABSA is a study that analyzes sentiment by aspects for multiple aspects that a text has. It is being studied in various forms depending on the purpose, such as analyzing all targets or just aspects and sentiments. Here, the aspect refers to the property of a target, and the target refers to the text that causes the sentiment. For example, for restaurant reviews, you could set the aspect into food taste, food price, quality of service, mood of the restaurant, etc. Also, if there is a review that says, "The pasta was delicious, but the salad was not," the words "steak" and "salad," which are directly mentioned in the sentence, become the "target." So far, in ABSA, most studies have analyzed sentiment only based on aspects or targets. However, even with the same aspects or targets, sentiment analysis may be inaccurate. Instances would be when aspects or sentiment are divided or when sentiment exists without a target. For example, sentences like, "Pizza and the salad were good, but the steak was disappointing." Although the aspect of this sentence is limited to "food," conflicting sentiments coexist. In addition, in the case of sentences such as "Shrimp was delicious, but the price was extravagant," although the target here is "shrimp," there are opposite sentiments coexisting that are dependent on the aspect. Finally, in sentences like "The food arrived too late and is cold now." there is no target (NULL), but it transmits a negative sentiment toward the aspect "service." Like this, failure to consider both aspects and targets - when sentiment or aspect is divided or when sentiment exists without a target - creates a dual dependency problem. To address this problem, this research analyzes sentiment by considering both aspects and targets (Target-Aspect-Sentiment Detection, hereby TASD). This study detected the limitations of existing research in the field of TASD: local contexts are not fully captured, and the number of epochs and batch size dramatically lowers the F1-score. The current model excels in spotting overall context and relations between each word. However, it struggles with phrases in the local context and is relatively slow when learning. Therefore, this study tries to improve the model's performance. To achieve the objective of this research, we additionally used auxiliary loss in aspect-sentiment classification by constructing CNN(Convolutional Neural Network) layers parallel to existing models. If existing models have analyzed aspect-sentiment through BERT encoding, Pooler, and Linear layers, this research added CNN layer-adaptive average pooling to existing models, and learning was progressed by adding additional loss values for aspect-sentiment to existing loss. In other words, when learning, the auxiliary loss, computed through CNN layers, allowed the local context to be captured more fitted. After learning, the model is designed to do aspect-sentiment analysis through the existing method. To evaluate the performance of this model, two datasets, SemEval-2015 task 12 and SemEval-2016 task 5, were used and the f1-score increased compared to the existing models. When the batch was 8 and epoch was 5, the difference was largest between the F1-score of existing models and this study with 29 and 45, respectively. Even when batch and epoch were adjusted, the F1-scores were higher than the existing models. It can be said that even when the batch and epoch numbers were small, they can be learned effectively compared to the existing models. Therefore, it can be useful in situations where resources are limited. Through this study, aspect-based sentiments can be more accurately analyzed. Through various uses in business, such as development or establishing marketing strategies, both consumers and sellers will be able to make efficient decisions. In addition, it is believed that the model can be fully learned and utilized by small businesses, those that do not have much data, given that they use a pre-training model and recorded a relatively high F1-score even with limited resources.