• Journal of Technology Innovation
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• v.18 no.2
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• pp.253-288
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• 2010

Production innovation capabilities are critical to the survival and growth of firms. This paper investigates industrial dynamics and dynamic capabilities of firms by looking at how an industry evolution process influences firms' product innovation strategy and how dynamic capabilities affect firms' product innovation process. Korea Wireless Internet industry shows a full cycle of industry evolution process including introduction phase, growth phase, maturity phase, and decline phase using by dynamic technological and market changes. 7 listed companies in Korea Wireless Internet industry were selected. We have conducted multiple case studies based upon in depth interviews. Empirical results show that different phases of industry evolution influence firms' strategy of product innovation. Dynamic capabilities are also appears to be very important to the survival and growth of a firm.

• Management & Information Systems Review
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• v.32 no.2
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• pp.1-19
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• 2013

The purpose of this study is to provide the model for goal planning system and explore the affecting factors in suitability of goal planning system and possibility of goal attainment. The findings in a factor analysis of the sample of Korean firms indicate that the factors suggested by theories and previous studies can be summarized to 40 implications and then 7 groups including attachment of top managers, systemicity, participation system, motivation, alignment of top-down & bottom-up. adjustment for environmental changes, goal content. In addition, the findings in multiple regression analyses show that goal content, alignment of top-down & bottom-up, and adjustment for environmental changes are positively, significantly influence the recognition level of employees on the suitability of goal planning system. On the other hand, goal content, attachment of top managers, and motivation are significantly influence the recognition level of employees on the possibility of goal attainment. This study provides the checklist for the suitability of goal planning system and various implications for practitioners.

• Journal of Distribution Research
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• v.15 no.5
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• pp.127-153
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• 2010

This study explores the locomotives of the growth in the Korean online shopping industry upon the theoretical basis based on the last 10 years' rapid changing environment. This attempt reveals the counter-arguments against preemtive effects based on the observation of reintermediation process in the online industry. We reviewed the NEBIC model proposed by Wheeler(2002) and propose the growth model, double helix framework based on the dynamic capability view. Furthermore the relevance of the proposed framework was validated with the review of last 10 years' sales and market share data in the online shopping industry. Meanwhile we found the limits of online market growth with the open market domination. So future of the online shopping retailers is depending on the development of the channel functions and merchandising on the basis of self-capability. Based on the tentative conclusion, we also suggest implications for the policy makers. Firstly policy facilitating the specialization of the power sellers incubased in the open market is necessary for the sustainable online market growth. And the establishment of the control tower is suggested to coordinate the consistency of the policies and regulations. And the device of the incentive is also proposed to strengthen the open markets' function to facilitate the small and medium online merchants.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.2
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• pp.609-615
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• 2014

Due to the recent global recession, the car industry demand levels have plummeted which led to a crisis in the automotive parts industry for the first time in history. Since the fourth quarter of 2008, the automotive parts manufacturers in America have faced a record loss and those in Japan and Europe who also had a strong track record are facing a weak economy. In addition, the domestic automotive parts industry is also affected by the global economic crisis. This research is that the relative efficiency analysis utilizing the DEA has done on the object of 25 small and medium-sized automotive parts manufacturers publicly listed, As the efficiency analysis result 6 of 25 manufacturers are efficient in CCR model and 12 manufactures have shown efficiency in BCC model, the efficiency analysis in consideration of the manufacturer size. The manufacturers with efficiency 1 in 25 manufacturers are DMU 1, 5, 7, 10, 18, 24 and the relatively benchmarking objects in other manufactures are DMU 1, 10, 24, Based on the results of this research, a direction to the domestic automotive parts manufacturers as well as a significant information will be provided in managing the companies in the future by the improvement of management efficiency through the practical efficiency analysis.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.1
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• pp.17-28
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• 2019

In the financial investment management strategy, the distributed investment selecting and combining various financial assets is called portfolio management theory. In recent years, the blockchain based financial assets, such as cryptocurrencies, have been traded on several well-known exchanges, and an efficient portfolio management approach is required in order for investors to steadily raise their return on investment in cryptocurrencies. On the other hand, deep learning has shown remarkable results in various fields, and research on application of deep reinforcement learning algorithm to portfolio management has begun. In this paper, we propose an efficient financial portfolio investment management method based on Asynchronous Advantage Actor-Critic (A3C), which is a representative asynchronous reinforcement learning algorithm. In addition, since the conventional cross-entropy function can not be applied to portfolio management, we propose a proper method where the existing cross-entropy is modified to fit the portfolio investment method. Finally, we compare the proposed A3C model with the existing reinforcement learning based cryptography portfolio investment algorithm, and prove that the performance of the proposed A3C model is better than the existing one.

• Communications of Mathematical Education
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• v.35 no.1
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• pp.97-118
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• 2021

Since the characteristics of teachers that affect mathematics academic achievement are constantly changing and affecting mathematics achievement, longitudinal studies that can predict and analyze growth are needed. This study used data from middle and high school students from 2013(first year of middle school) to 2017(second year of high school) of the Seoul Education Longitudibal Study(SELS). By classifying the longitudinal changes in mathematics academic achievement into similar subgroups, the direct influence of teachers' characteristics(professionalism, expectations, academic feedback) perceived by students on the longitudinal changes in mathematics academic achievement was examined. As a result of the study, it was found that the characteristics of mathematics teachers(professional performance, expectation, and academic feedback) in group 1(343 students), which included the top 14.5% of students, did not directly affect longitudinal changes in mathematics academic achievement. Students in the middle 2nd group(745, 32.2%) had academic feedback from the mathematics teacher, and the 2nd group(1225 students) in the lower 53%, which included most of the students, showed that the expectations of the mathematics teacher were the longitudinal mathematics achievement. The change has been shown to have a direct effect. This suggests that support for teaching and learning should also reflect this, as the direct influence of teachers' professionalism, expectations, and academic feedback on longitudinal changes in mathematics academic achievement is different according to the characteristics and dispositions of students.

• Communications of Mathematical Education
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• v.34 no.4
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• pp.525-544
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• 2020

There are many factors that affect academic achievement, and the influences of those factors are also complex. Since the factors that influence mathematics academic achievement are constantly changing and developing, longitudinal studies to predict and analyze the growth of learners are needed. This study uses longitudinal data from 2014 (second year of middle school) to 2017 (second year of high school) of the Seoul Education Longitudibal Study, and divides it into groups with similar longitudinal patterns of change in mathematics academic achievement. The longitudinal change patterns and direct influence of mood and satisfaction were examined. As a result of the study, it was found that the mathematics academic achievement of the first group (1456 students, 68.3%) including the majority of students and the second group (677 students) of the top 31.7% had a direct influence on the mathematics class attitude. It was found that the mood and satisfaction of mathematics classes did not have a direct effect. In addition, the influence of mathematics class attitude on mathematics academic achievement was different according to the group. In addition, students in group 2 with high academic achievement in mathematics showed higher mathematics class attitude, mood, and satisfaction. In addition, the attitude, atmosphere, and satisfaction of mathematics classes were found to change continuously from the second year of middle school to the second year of high school, and the extent of the change was small.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.2
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• pp.47-65
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• 2020

With the era of the Fourth Industrial Revolution in sight, innovative business models utilizing new technologies are emerging, and startups are enjoying an abundance of opportunities based on the agility to respond to disruptive innovations and the opening to new technologies. However, what is most important in creating a sustainable start-up ecosystem is not the start-up itself, but the process of research-start-investment-investment-the leap to listing and big business-in order to build a virtuous circle of startups that leads to re-investment. To this end, the environment created in the hub area where start-ups were conducted is important, and these material and non-material environmental factors are described as being inclusive by the word "entrepreneurial ecosystem." This study aims to provide implications for Korea's entrepreneurial ecosystem through the study of the interaction of the elements that make up the start-up ecosystem and the relationship of ecosystem participants in Singapore. Singapore has been consistently mentioned as the top two Asian countries in assessing the start-up environment and business environment. In this process, six elements of the entrepreneurial ecosystem presented by Isenberg(2010)-policies, finance, culture, support, human resources, and market-are the best frameworks for analyzing entrepreneurial ecosystems in terms of well encompassing prior studies related to entrepreneurial ecosystem elements, and a model of regional transformation is formed focusing on some elements to suit Singapore, the target area of study. By considering that Singapore's political nature would inevitably have a huge impact on finance, Smart Nation policy was having an impact on university education related to entrepreneurship, and that the entrepreneurial networks and global connectivity formed within Singapore's start-up infrastructure had a significant impact on Singapore's start-up's performance, researches needed to look more at the factors of policy, culture and market. In addition, qualitative research of participants in the entrepreneurial ecosystem was essential to understand the internal interaction of the elements of the start-up ecosystem, so the semi-structured survey was conducted by visiting the site. As such, this study examined the status of the local entrepreneurial ecosystem based on qualitative research focused on policies, culture and market elements of Singapore's start-up ecosystem, and intended to provide implications for regulations related to start-ups, the role of universities and start-up infrastructure through comparison with Korea. This could contribute not only to the future research of the start-up ecosystem, but also to the creation of a start-up infrastructure, boosting the start-up ecosystem, and the establishment of the orientation of the start-up education in universities.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.