• The Journal of the Korea Contents Association
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• v.6 no.11
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• pp.38-44
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• 2006

With the expansion of internet diffusion, the digital content industry in such sector game, animation and e-learning shows rapid growth and, accordingly it becomes important for policy makers to provide its technical R&D directions ensuring successful commercialization. This paper provides details on the business and industry assessment, and further, proposes substantial support over industries as well as academia. The Business Opportunity Engineering(BOE) process, revised and systemized assessment frame of the seven steps of SRIC-BI process, has been applied to assess and compare the digital content business and industry.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.4
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• pp.109-120
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• 2022

This study has incorporated the mechanics-dynamics-emotions (MDE) and two behavioral learning paths to investigate the customers' co-creation behavior in Taiwan. The intuitive path begins with a gamification design that reflects the customers' proactive and innovative behavior; the cognitive path begins with persuasion knowledge remarks based on rational and reactive reasoning. These two paths conclude what forms user co-creation. The study collects data of 505 active social media users in Taiwan and employs structural equation modeling. The empirical findings demonstrate persuasive knowledge and gamification design are significantly associated with self-reference, and in turn, positively associated with co-creation. It indicates that cognitive behavior plays the main role in forming co-creation. Participants are more drawn to co-creation behaviors by the marketing contents that prompt reactive behaviors than proactive ones. Therefore, marketing managers can use appropriate stimuli to enhance co-creation behavior. Companies can design activities related to users, and more accessible for reactive, instead of proactive behavior, i.e., asking for their initiatives. It also suggests that companies' marketing campaigns should involve key opinion leaders matching the product image and the target audience's preferences. The novelty of this study is to introduce a novel augmented MDE framework to extend the "dynamics" into the incubation and implementation stage.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.07a
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• pp.207-208
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• 2017

본 논문에서는 유아교육기관에서 유치원 교사들이 멀티미디어를 활용하는데 있어서 학습 능력 향상 및 소프트웨어 활용 기술을 향상시키기 위해 플립러닝을 진행하도록 한다. 이를 통해 최신 멀티미디어의 특성을 잘 활용한 교수자의 강의 보다는 플립러닝을 위한 참여자 위주의 수업을 진행한다. 새롭게 등장하는 멀티미디어 기술을 접해야 하는 과목의 특성과 창의적이고 융합적인 인재를 양성해야하는 유치원 교사의 역할을 접목하여 미래 사회를 이끄는 유아들에게 효율적인 학습이 이루어지도록 하는 데에 큰 의의를 둔다. 유치원 교사들은 멀티미디어를 활용한 교수매체 활용 능력을 향상시키는 것은 물론이고, 최신 미디어 기술을 수업에 적극 활용하여 4차 산업혁명 시대의 흐름에 맞는 교과과정을 운영하도록 한다, 따라서 본 연구는 유치원 교사들을 양성하는데 있어서 미디어 교육의 중요성을 알리고 이를 교과과정에서 운영할 수 있도록 플립러닝을 활용한 교육과정을 제시하고자 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.538-561
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• 2020

Recommender Systems (RecSys) have a major role in e-commerce for recommending products, which they may like for every user and thus improve their business aspects. Although many types of RecSyss are there in the research field, the state of the art RecSys has focused on finding the user similarity based on sequence (e.g. purchase history, movie-watching history) analyzing and prediction techniques like Recurrent Neural Network in Deep learning. That is RecSys has considered as a sequence prediction problem. However, evaluation of similarities among the customers is challenging while considering temporal aspects, context and multi-component ratings of the item-records in the customer sequences. For addressing this issue, we are proposing a Deep Learning based model which learns customer similarity directly from the sequence to sequence similarity as well as item to item similarity by considering all features of the item, contexts, and rating components using Dynamic Temporal Warping(DTW) distance measure for dynamic temporal matching and 2D-GRU (Two Dimensional-Gated Recurrent Unit) architecture. This will overcome the limitation of non-linearity in the time dimension while measuring the similarity, and the find patterns more accurately and speedily from temporal and spatial contexts. Experiment on the real world movie data set LDOS-CoMoDa demonstrates the efficacy and promising utility of the proposed personalized RecSys architecture.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.97-110
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• 2018

With the advent of the 4-th industrial revolution, manufacturing companies have increasing interests in the realization of smart manufacturing by utilizing their accumulated facilities data. However, most previous research dealt with the structured data such as sensor signals, and only a little focused on the unstructured data such as text, which actually comprises a large portion of the accumulated data. Therefore, we propose an association rule mining based facility error pattern extraction framework, where text data written by operators are analyzed. Specifically, phrases were extracted and utilized as a unit for text data analysis since a word, which normally used as a unit for text data analysis, is unable to deliver the technical meanings of facility errors. Performances of the proposed framework were evaluated by addressing a real-world case, and it is expected that the productivity of manufacturing companies will be enhanced by adopting the proposed framework.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.287-308
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• 2023

After launching its service in November 2022, ChatGPT has rapidly increased the number of users and is having a significant impact on all aspects of society, bringing a major turning point in the history of artificial intelligence. In particular, the inference ability of large language models such as ChatGPT is improving at a rapid pace through prompt engineering techniques. This reasoning ability can be considered as an important factor for companies that want to adopt artificial intelligence into their workflows or for individuals looking to utilize it. In this paper, we begin with an understanding of in-context learning that enables inference in large language models, explain the concept of prompt engineering, inference with in-context learning, and benchmark data. Moreover, we investigate the prompt engineering techniques that have rapidly improved the inference performance of large language models, and the relationship between the techniques.

• Asia Marketing Journal
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• v.15 no.3
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• pp.79-95
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• 2013

New product innovation is a process of embodying new knowledge in a product and technology licensing is getting popular as a means to innovations and introduction of new product to the market in today's competitive global market environment. Incumbents often rely on technology licensing to access new product opportunities created by other firms. Prior research has examined various aspects of technology licensing agreements such as specific contract terms of licensing agreements, e.g., distribution of control rights, exclusivity of licensing agreements, cross-licensing, and the scope of licensing agreements. This study aims to provide answers to an important, but under-researched question: why do some incumbents initiate more licensing agreement for exploratory learning while others do it for exploitative learning along the innovation process? We attempt to extend our knowledge of licensing agreements from an organizational learning perspective. Technology licensing as a specific form of interfirm linkages can be initiated with different learning objectives along the process of new product innovation. The exploratory stages of the innovation process such as discovery or research stages involve extensive searches to create new knowledge or capabilities, whereas the exploitative stages of the innovation process such as application or test stages near the commercialization are more focused on developing specific applications or improving their efficiency or reliability. Thus, different stages of the innovation process generate different types of learning and the resulting technological resources. We examine when incumbents as licensees initiate more licensing agreements for exploratory learning objectives and when more for exploitative learning objectives, focusing on two factors that may influence a firm's formation of exploratory and exploitative licensing agreements: 1) its past radical and incremental innovation experience and 2) its internal investments in R&D and marketing. We develop and test our hypotheses regarding the relationship between a firm's radical and incremental new product experience, R&D investment intensity and marketing investment intensity, and the likelihood of engaging in exploratory and exploitive licensing agreements. Using data collected from various secondary sources (Recap database, Compustat database, and FDA website), we analyzed technology licensing agreements initiated in the biotechnology and pharmaceutical industries from 1988 to 2011. The results of this study show that incumbents initiate exploratory rather than exploitative licensing agreements when they have more radical innovation experience and when they invest in R&D activities more intensively; in contrast, they initiate exploitative rather than exploratory licensing agreements when they have more incremental innovation experience and when they invest in marketing activities more intensively. The findings of this study contribute to the licensing and interfirm cooperation studies. First, this study lays a foundation to understand the organizational learning aspect of technology licensing agreements. Second, this study sheds lights on how a firm's internal investments in R&D and marketing are linked to its tendency to initiate licensing agreements along the innovation process. Finally, the findings of this study provide important insight to managers regarding which technologies to gain via licensing agreements. This study suggests that firms need to consider their internal investments in R&D and marketing as well as their past innovation experiences when they initiate licensing agreements along the process of new product innovation.

• 한국경영정보학회:학술대회논문집
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• 2008.06a
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• pp.446-453
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• 2008

All are organizations embedded, here in after, Em-organizaion that confronts the ever-growing complexity. It is important to know Em-organization through Individual Mastery. The complexity must be decreased, and clarified in order to derive to get our ontology from the influence of others. The opportunity to learn in practice is embedded in processes that the community developed. Driving strategic innovation is achieving breakthrough performance throughout the value chain. We used to express complex unit on matrix which includes only the federal statutes because the role of information technology should be a source of competitive advantages each other. Therefore, we got the idea that integrated both kinds of knowledge to create differentiation by ourselves. This practice is situated the learning of Strategic CoP in e-class seminar of our graduate school. We suggest theoretically two things. One is matrix-based decision. Another is creating new context through systems thinking.

• International Journal of Contents
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• v.10 no.3
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• pp.64-72
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• 2014

This study explored the elements that affect team achievements in virtual environments. In this study, consideration was given to the role of group dynamics in facilitating productive interaction. We aspired to reveal the mechanisms of group dynamics and examined how group dynamics affected team achievements in virtual environments. The empirical study was performed with undergraduate students enrolled in an e-learning course. In collaboration with other majors, students executed team projects and managed project issues in forums or chat rooms. The results of the empirical study indicated that leadership, creative friction, and group cohesion (components of group dynamics) had positive relationships with team achievements. The findings confirmed that addressing creative conflict is a method to improve team performance and that leadership is a key factor in project teams.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.93-97
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• 2002

In this paper, we propose personalized recommendation techniques based on multidimensional scaling (MDS) method for Business to Consumer Electronic Commerce. The multidimensional scaling method is traditionally used in marketing domain for analyzing customers' perceptional differences about brands and products. In this study, using purchase history data, customers in learning dataset are assigned to specific product categories, and after then using MDS a positioning map is generated to map product categories and alternative advertisements. The positioning map will be used to select personalized advertisement in real time situation. In this paper, we suggest the detail design of personalized recommendation method using MDS and compare with other approaches (random approach, collaborative filtering, and TOP3 approach)