• Information Systems Review
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• v.21 no.1
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• pp.135-152
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• 2019

Recently there are many development and support policies for start-up companies because of successful venture companies related to ICT services. However, as these policies have focused on the support for the initial stage of start-up, many start-up companies have difficulties to continuously grow up. The main reason for these difficulties is that they recognize start-up tasks as independent activities. However, many experts or related articles say that start-up tasks are composed of related processes from the initial stage to the stable stage of start-up firms. In this study, we models the start-up processes based on the survey collected by the start-up companies, and analyze the start-up process of ICT service companies with process mining techniques. Through process mining analysis, we can draw a sequential flow of tasks for start-ups and the characteristics of them. The analysis of start-up businessman, idea derivation, creating business model, business diversification processes are resulted as important processes, but marketing activity and managing investment funds are not. This result means that marketing activity and managing investment funds are activities that need ongoing attention. Moreover, we can find temporal and complementary tasks which could not be captured by independent individual-level activity analysis. Our process analysis results are expected to be used in simulation-based web-intelligent system to support start-up business, and more cumulated start-up business cases will be helpful to give more detailed individual-level personalization service. And our proposed process model and analyzing results can be used to solve many difficulties for start-up companies.

• Tunnel and Underground Space
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• v.33 no.4
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• pp.209-227
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• 2023

The government continues to announce measures to revitalize smart construction technology based on BIM for productivity innovation in the construction industry. In the design phase, the goal is design automation and optimization by converging BIM Data and other advanced technologies. Accordingly, in the basic design of the Namhae Seomyeon-Yeosu Sindeok National Road Construction Project, a domestic undersea tunnel project, BIM-based design was carried out by developing tunnel design automation technology using 3D spatial information according to the tunnel design process. In order to derive the optimal alignment, more than 10,000 alignment cases were generated in 36hr using the generative design technique and a quantitative evaluation of the objective functions defined by the designer was performed. AI-based ground classification and 3D Geo Model were established to evaluate the economic feasibility and stability of the optimal alignment. AI-based ground classification has improved its precision by performing about 30 types of ground classification per borehole, and in the case of the 3D Geo Model, its utilization can be expected in that it can accumulate ground data added during construction. In the case of 3D blasting design, the optimal charge weight was derived in 5 minutes by reviewing all security objects on the project range on Dynamo, and the design result was visualized in 3D space for intuitive and convenient construction management so that it could be used directly during construction.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.53-71
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• 2024

Generative Artificial Intelligence (AI) has become the focus of societal attention due to its wide range of applications and profound impact. This paper constructs a comprehensive theoretical model based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), integrating variables such as Personal Innovativeness and Perceived Risk to study the key factors influencing enterprises' adoption of Generative AI. We employed Structural Equation Modeling (SEM) to verify the hypothesized paths and used the Bootstrapping method to test the mediating effect of Behavioral Intention. Additionally, we explored the moderating effect of Perceived Risk through Hierarchical Regression Analysis. The results indicate that Performance Expectancy, Effort Expectancy, Social Influence, Price Value, and Personal Innovativeness have significant positive impacts on Behavioral Intention. Behavioral Intention plays a significant mediating role between these factors and Use Behavior, while Perceived Risk negatively moderates the relationship between Behavioral Intention and Use Behavior. This study provides theoretical and empirical support for how enterprises can effectively adopt Generative AI, offering important practical implications.

• Information Systems Review
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• v.26 no.1
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• pp.193-218
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• 2024

This study identified factors related to the technological characteristics of conversational generative AI services and the user's task characteristics. Then, it analyzed the effects of task-technology fit on user satisfaction and continued use. The effects of trust, which represents the degree of users' belief in the information provided by generative AI, on task-technology fit, user satisfaction, and user continuance intention were also examined. A survey was conducted among users of various age groups, and 198 questionnaires were collected and analyzed using SmartPLS 4.0 to validate the proposed model. As a result of hypothesis testing, it was confirmed that language fluency and interactivity among technology characteristics and ambiguity among task characteristics significantly affect user satisfaction and intention to continue using via task-technology fit. However, creativity among skill characteristics and time flexibility among task characteristics did not significantly affect task-technology fit, and trust did not directly affect task-technology fit and intention to continue using, but only positively affected user satisfaction. The results of this study can provide meaningful implications for vendors who want to develop and provide conversational generative AI services or companies who want to adopt generative AI technology to improve business productivity.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.81-88
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• 2024

Despite the rapid growth of the generative AI market and significant interest from domestic companies and institutions, concerns about the provision of inaccurate information and potential information leaks have emerged as major factors hindering the adoption of generative AI. To address these issues, this paper designs and implements a question-answering system based on the Retrieval-Augmented Generation (RAG) architecture. The proposed method constructs a knowledge database using Korean sentence embeddings and retrieves information relevant to queries through optimized searches, which is then provided to the generative language model. Additionally, it allows users to directly manage the knowledge database to efficiently update changing business information, and it is designed to operate in a private network to reduce the risk of corporate confidential information leakage. This study aims to serve as a useful reference for domestic companies seeking to adopt and utilize generative AI.

• Smart Media Journal
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• v.12 no.8
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• pp.60-68
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• 2023

After COVID-19, hotel industry is rapidly experiencing changes in the business environment, and under the influence of the Fourth Industrial Revolution, hotel industry is striving to secure competitive advantages through differentiation, including the use of big data and the IoT in service provision, as well as the introduction of artificial intelligence(AI) robot services. This study analyzed the perceived value of AI robot services and their impact on usage attitudes and behavioral intentions of customers who have used hotels that have introduced AI robot services. The results of the study showed that the value of robot services perceived by customers who have used robot services in hotels is categorized into three dimensions: social, experiential, and functional, and all of them have a positive effect on usage attitudes, with social, functional, and experiential values having a positive effect on usage attitudes in that order. Attitude toward use was also analyzed to have a positive effect on behavioral intention, which is consistent with the value-attitude-behavior model. Therefore, it is necessary for hotels to improve the satisfaction of hotel guests through diversified services of AI robot service.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.3
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• pp.44-58
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• 2017

The purpose of this study is to prove the Korean Internet company, Kakao's resilience capacity. For it, this paper reviews the previous literatures regarding Kakao's business models and discusses 'resilience' theory. Then, it organizes the research questions based on the theoretical background and explains the research methodology. It investigates the case of Kakao's business and organization. The case analysis shows that five levers of resilience are a good indicator for a successful platform business evolution. The five levers are composed of coordination, cooperation, clout, capability, and connection: First lever, coordination that makes the company to restructure its silo governance in order to respond to actual business flow starting from the basic asset like game and music content; second lever, cooperation where the firm provides creative people with playground for startups such as KakaoPage; third lever, clout where the company shares its data by opening its API of AI and chatbot to $3^{rd}$ party developers; fourth lever, capability where the firm establishes AI R&D center, KakaoBrain as the function of multi-domain generalist for developing diverse platforms tackling customer needs; and the last fifth lever, connection where the firm continues to expand its platform business to the peripheries, O2O businesses such as KakaoTaxi, KakaoOrder, KakaoPay, and KakaoBank. In conclusion, this study proposes Internet companies to be a resilient platform utilizing those five levers of resilience in order to form successful platform. This study contributes to the agile innovation of Internet platform with ecological sense.

• The Journal of Industrial Distribution & Business
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• v.11 no.10
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• pp.7-16
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• 2020

Purpose: It suggests that making a policy and strategies in a way of AI and its impact of commercialization on economic efficiency, social custom ethics. Research design, data, and methodology: The paper has analyzed the data based on the proposed model when derived as AI vs. FI job, etc. It is very different for each professional evaluation, which is artificial intelligence or robot job. One concept case was selected as a substitute job, with a relatively low level of occupation ability, such as direct labors, easily replaced. By the induction data has resulted in modeling. Results: The paper suggests that AI at high level become something how to make real decisions on ethical value modeling. Through physical simulation with the deduction data, it can be tuned to design and control what has not been solved, from human senses to climate. Conclusion: For the exploiting of new AI decision-making jobs in markets, the deduction data is possible to prove to AI's Decision-making that the percentage who can easily have different leadership as is different for each person. what is generated by some information silos may be applied to occupation societies. The empirical results indicate the deduction data that if AI determines ethical decisions (VC) for that modifications, it may replace future jobs.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.936-937
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• 2023

본 논문에서는 LLM(Large Language Model) 모델의 fine-tuning 을 통한, 기초 수리 서술형 문항 풀이용 모델 및 Dall-E2 등 이미지 생성형 모델을 활용한 따른 영어 퀴즈풀이용 이미지 생성형 모델을 생성하여, 한국어 기반 LLM 자체 모델 학습 및 교육용 이미지 생성에 대한 방법을 고찰하였다.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.57-66
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• 2023

In this study, we propose a novel research framework for the recommendation system that can estimate the user's emotional state and reflect it in the recommendation process by applying deep learning techniques and emotion AI (artificial intelligence). To this end, we build an emotion classification model that classifies each of the seven emotions of angry, disgust, fear, happy, sad, surprise, and neutral, respectively, and propose a model that can reflect this result in the recommendation process. However, in the general emotion classification data, the difference in distribution ratio between each label is large, so it may be difficult to expect generalized classification results. In this study, since the number of emotion data such as disgust in emotion image data is often insufficient, correction is made through augmentation. Lastly, we propose a method to reflect the emotion prediction model based on data through image augmentation in the recommendation systems.