• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.159-174
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• 2023

In this paper, we proposed a methodology to effectively determine the selection and prioritization of new business and innovation projects using AI technology. AI technology is a technology that can upgrade the business of companies in various industries and increase the added value of the entire industry. However, there are various constraints and difficulties in the decision-making process of selecting and implementing AI projects in the enterprise. In this paper, we propose a new methodology for prioritizing AI projects using Morphology, AHP, and TOPSIS. The proposed methodology helps prioritize AI projects by simultaneously considering the technical feasibility of AI technology and real-world user requirements. In this study, we applied the proposal methodology to a real enterprise that wanted to prioritize multiple AI projects in the HR field and evaluated the results. The results confirm the practical applicability of the methodology and suggest ways to use it to help companies make decisions about AI projects. The significance of the methodology proposed in this study is that it is a framework for prioritizing multiple AI projects considered by a company in the most reasonable way by considering both business and technical factors at the same time.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.107-120
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• 2022

The purpose of this study is to analyze the net effect and the combined effect of the determinants of the likelihood of start-up success of researchers at public research institutes. Based on the existing literature, the determinants of the researcher's likelihood of start-up success were reviewed, and a conceptual relationship between the determinants of the likelihood of start-up success was established. Data collection was conducted through a survey targeting researchers at public research institutes, and a total of 114 data were collected. The partial least squares (PLS) analysis method was used to analyze the net effect of the likelihood of start-up success determinant, and the fuzzy-set qualitative comparative analysis (fsQCA) was used to analyze the combined effect of the likelihood of start-up success determinant. In the PLS analysis results, it was found that technology commercialization probability and creative self-efficacy had a significant positive effect independently on the likelihood of start-up success. In the fsQCA results, we found a combined effect of increasing the likelihood of start-up success when the technology commercialization probability, technology commercialization capability, and creative self-efficacy were high. These research results provide academic implications for understanding the determinants of the likelihood of start-up success of researchers in public research institutes.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.5
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• pp.31-47
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• 2022

The TIPS, a representative public-private cooperative project to revitalize the start-up ecosystem, is a government supported policy that promotes successful commercialization through various start-up support for technology-based startups. The purpose of this study is to analyze the investment decision factors of the TIPS program and to derive priorities. In order to achieve the research purpose, first, the investment decision factors were derived through literature analysis, a Delphi surveys were conducted on investors and experts participating in the evaluation of the TIPS program, and an AHP analysis was conducted on 20 VCs to empirically analyze the priority of factors on investment decisions. As a result of the analysis, the importance of critical factors was confirmed in the order of entrepreneurs(team) > market > product/service > finance > network. The importance of detailed factors was found in the order of entrepreneur's reliability and authenticity > market growth and scalability > team members' expertise and capabilities > adequacy of current market size > new market creation. This study presented the capabilities of technology-based startups preparing to participate in the TIPS program by deriving factors that influence investment decisions from an investor's perspective and comparing and analyzing the importance. It is also meaningful that basic data on determinants of private-led investment decision-making were presented to stake-holders such as venture capital, accelerator, and start-up support institutions.

• Knowledge Management Research
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• v.24 no.4
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• pp.219-235
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• 2023

The advancement of digital technology and the impact of COVID-19 have brought about changes in corporate innovation and organizational culture, thereby highlighting the significance of Smart Learning in the field of HRD (Human Resource Development). This trend has led to an increased interest in personalized Smart Learning among employees due to the growth of hybrid work and the widespread adoption of smart work practices. This study aimed to illuminate the relative importance of the factors that constitute Smart Learning from the perspective of HRD practitioners. Through a review of prior literature, Smart Learning hierarchy and factors most fitting to the current context were identified, and their relative importance was determined using the AHP method. Consequently, in the first-tier factors, importance was confirmed in the order of 'Learning Activities', 'Teaching Activities', 'Learning Content', 'Assessment and Evaluations', and 'Learning Time and Space'. At the second-tier encompassing all factors, 'Pedagogical Strategy', 'Learning Results', 'Learning Tasks', 'Learning Goal', and 'Learning Support' emerged within the top five factors. These findings are significant in that they redefine the concept of smart learning and propose an academic framework for future research. Additionally, from a practical perspective, it is anticipated that this study will contribute valuable insights for HRD practitioners, aiding them in focusing on which factors to prioritize for enhancing and advancing Smart Learning initiatives.

• Journal of the Economic Geographical Society of Korea
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• v.26 no.4
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• pp.458-476
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• 2023

This study analyzes previous research on 'The Self-Governance of the Commons' to overcome 'The Tragedy of the Commons', and derives elements for successful commons management. These factors are compared and analyzed with the social and economic attributes of the Jeju Haenyeo community, a successful community self-governance model. In addition, in the recently changing environment, it is revealed whether this internal community mechanism can be useful in the future. The goal is to reveal what social and economic factors will help the sustainability of the Jeju haenyeo community in the future. As a result of analyzing the internal operating mechanism of the Jeju haenyeo community, the production and distribution system that improves trust and reciprocity, the inherent sense of community, the division of roles between formal and informal organizations, and the institutionalized explicit and implicit norms within the organization served as internal and external strengths of community sustainability. However, the closure of the network, the crisis of productivity, the weakening of homogeneity, and the emergence of new subjects acted as internal and external weaknesses. In conclusion, for the sustainability of the Jeju Haenyeo community, it is necessary to reorganize the reproductive function of labor using the haenyeo school, to maintain clarity on the subject of livelihood and cultural transmission, and guarantee the income of Haenyeo.

• Food Science and Preservation
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• v.30 no.3
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• pp.459-471
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• 2023

The Bayesian network (BN) model was applied to analyze the characteristic variables that affect compliance with safety inspections of farmed eel during the production stage, using the data from 30,063 cases of eel aquafarm safety inspection in the Integrated Food Safety Information Network (IFSIN) from 2012 to 2021. The dataset for establishing the BN model included 77 non-conforming cases. Relevant HACCP data, geographic information about the aquafarms, and environmental data were collected and mapped to the IFSIN data to derive explanatory variables for nonconformity. Aquafarm HACCP certification, detection history of harmful substances during the last 5 y, history of nonconformity during the last 5 y, and the suitability of the aquatic environment as determined by the levels of total coliform bacteria and total organic carbon were selected as the explanatory variables. The highest achievable eel aquafarm noncompliance rate by manipulating the derived explanatory variables was 24.5%, which was 94 times higher than the overall farmed eel noncompliance rate reported in IFSIN between 2017 and 2021. The established BN model was validated using the IFSIN eel aquafarm inspection results conducted between January and August 2022. The noncompliance rate in the validation set was 0.22% (15 nonconformances out of 6,785 cases). The precision of BN model prediction was 0.1579, which was 71.4 times higher than the non-compliance rate of the validation set.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.137-142
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• 2024

Recently, deep learning technology has become those methods as de facto standards in the area of medical data representation. But, deep learning inherently requires a large amount of training data, which poses a challenge for its direct application in the medical field where acquiring large-scale data is not straightforward. Additionally, brain signal modalities also suffer from these problems owing to the high variability. Research has focused on designing deep neural network structures capable of effectively extracting spectro-spatio-temporal characteristics of brain signals, or employing self-supervised learning methods to pre-learn the neurophysiological features of brain signals. This paper analyzes methodologies used to handle small-scale data in emerging fields such as brain-computer interfaces and brain signal-based state prediction, presenting future directions for these technologies. At first, this paper examines deep neural network structures for representing brain signals, then analyzes self-supervised learning methodologies aimed at efficiently learning the characteristics of brain signals. Finally, the paper discusses key insights and future directions for deep learning-based brain signal analysis.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.149-156
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• 2024

The increased intelligence and speed of vehicle infotainment, whose main purpose was emergency and external communication, is showing the potential for application to various services such as navigation and autonomous driving. In particular, functionality for linking external devices and infrastructure is being strengthened due to advances in communication and networks. Under this trend, it is necessary to consider the direction of linkage with the cooperative intelligent transportation system (C-ITS) for advanced vehicle services and driving. In addition, in the case of automobiles, future vehicle development concepts are being established based on the concept of software-defined vehicles (SDVs) in line with the trend of electrification beyond telematics and infotainment advancements, and such SDV linkage must be considered at the same time. In this paper, we consider the future direction of ITS and SDV linkage based on analysis of vehicle infotainment structure. First, for this purpose, we analyze the existing vehicle infotainment structure and architecture, and also present the structure of the SDV linked to it. Based on this, analysis and implications are drawn on the possibility of applying and linking standard-based C-ITS services with SDV devices.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.27 no.2
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• pp.17-28
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• 2024

In order to achieve the management goals (30by30) mandated by the Kunming-Montreal Global Biodiversity Framework, this study established the site-level assessment criteria for OECMs, tailored to domestic circumstances using the Delphi analysis. Subsequently, a site-level assessment was conducted on the National Gariwangsan Natural Recreation Forest. As a result of the study, the initial step involved presenting criteria for the site-level assessment of OECMs, with 'consent for the assessment and recognition of OECM by competent and management authority' proposed as a prerequisite. Subsequently, seven evaluation criteria were established, including 'other than a legally protected area', 'spatially separated area with defined boundaries', 'effective in-situ conservation of biodiversity', 'sustainable management based on the competent and management authority', 'long-term sustainability of conservation outcomes', and 'provision of ecosystem services'. The results of applying site-level assessment criteria to the National Gariwangsan Natural Recreation Forest indicate that six criteria were met, while one criterion (sustainable management based on the competent and management authority) requires further improvement. Specifically, the key competent and management authorities for the National Gariwangsan Natural Recreation Forest are the Korea Forest Service and the National Natural Recreation Forest Management Office, with competent and management organizations established. However, the management focus is primarily on providing forest recreation services centered on users and facilities, making it difficult to confirm the long-term biodiversity conservation plans and implementation by the competent and management authorities. Therefore, it is deemed necessary to improve the long-term biodiversity conservation plans and implementation for the recognition of the National Gariwangsan Natural Recreation Forest as an OECM.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.157-172
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• 2024

Autonomous driving technology, when commercialized, has the potential to improve the safety, mobility, and environmental performance of transportation networks. However, safe autonomous driving may be hindered by poor sensor performance and limitations in long-distance detection. Therefore, cooperative autonomous driving that can supplement information collected from surrounding vehicles and infrastructure is essential. In addition, since HDVs, AVs, and CAVs have different ranges of perceivable information and different response protocols, countermeasures are needed for mixed traffic that occur during the transition period of autonomous driving technology. There is a lack of research on traffic flow optimization that considers the penetration rate of autonomous vehicles and the different characteristics of each road segment. The objective of this study is to develop weights based on safety, operational, and environmental factors for each infrastructure control use case and autonomous vehicle MPR. To develop an integrated evaluation index, infra-guidance AHP and hybrid AHP weights were combined. Based on the results of this study, it can be used to give right of way to each vehicle to optimize mixed traffic.