• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1341-1346
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• 2015

Nowadays, many small businesses have experienced the failure of business or hardship. In this point, specific and integrated information for startup business should be required to decrease the rate of failure and to increase the rate of success. This study is to suggest the integration of various data which various public institutions have separately. For this purpose, it is to classify the data types in constructing big-data for start-up business and to suggest a way of data integration, analysis method, and web or services of information system for supporting startup businesses.

• East Asian Journal of Business Economics (EAJBE)
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• v.10 no.1
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• pp.109-121
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• 2022

Purpose - This study attempted to analyze news big data in order to examine the trend of change in housework due to technological innovation and family changes. Research design, data, and methodology - News big data was collected from Bigkinds for the purpose of trend analysis. A total of 8,270 articles containing 'housework' were extracted from news articles between January 1, 1990 and December 31, 2021. 11 general daily newspapers and 8 business newspapers were selected and were analyzed by dividing them into five-year units. Result - The change of trends in housework that appeared through news big data analysis can be summarized as below. First, the tendency to regard housework as work of women or housewives is gradually weakening. Instead, the centrality of connection with double income is increasing. Second, there is a tendency to strengthen the institutional approach to evaluation of the productivity of housework. Third, the possibility of market substitution for housework is expanding. Conclusion - In the era of the 4th industrial revolution, examining the impact of technological innovation and family change on housework not only enables the prospect of an industry, but also provides implications for policies related to housework. In addition, this study is differentiated in that it contributed to expand the field of housework research previously limited to analyzing survey data.

• Journal of Fashion Business
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• v.24 no.4
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• pp.144-160
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• 2020

Today, the development of innovative technologies is accompanied by changes in the industrial structure and the Big Blur phenomenon, where the boundaries in various fields are blurred. The purpose of this study was to view the Big Blur phenomenon as a big paradigm shift in the 21st century and derive environmental changes and characteristics of the Korean fashion market. The research method included an analysis of the fashion brands after 2015. Through this study, we intended to establish a framework for understanding the changes in the fashion market from the perspective of Big Blur and discuss the direction of brand marketing. The research results showed the hyperlinks, connectivity, openness, homeostasis, synchronicity, mobility, interactivity, and brand experience of online and offline spaces beyond the boundaries of virtual space and offline physical spaces such as online physical and spatial viewpoints. It also showed the characteristics. The characteristics from the socio-cultural point of view were characteristic of diversity, mixture, coexistence, composability, and pluralism beyond the traditional socio-cultural and regulatory scopes. Hip hop fashion, street fashion, unisex, genderless, androgynous fashion, and kid fashion are the backbone of the Big Blur and are becoming important factors in fashion. The characteristics of the market and economic viewpoint are prosumers that play roles both as producers and consumers. It shows the extensibility of consumers as producers, the cohesiveness of producers and consumers, the cooperation, and the interconnectivity.

• Journal of Digital Convergence
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• v.13 no.3
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• pp.55-63
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• 2015

South Korean Government confirmed the creation of competency-centered society as a key national issue and announced development and utilization plan of NCS(National Competency Standards) On May 21, 2013. As a part of the government's plans, they had been developed NCS about 833 jobs by 2014. But Big Data related job, as an emerging job, cannot be seen as a reliable form of job yet. As, at the major industrialized countries and the domestic, education and job competency models of knowledge and skills to take advantage of various types of Big Data have coming, it is a situation that is certainly not settled and more or less in confusion. In this study, for the purpose to present the Big Data Competency reference model for companies and organizations to effectively leverage Big Data, we have presented this reference model and summarized competency elements units such as 20 knowledges and 15 skills of Big Data competency.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.01a
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• pp.277-278
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• 2015

The volume sub-challenge requires novel approaches, often referred to as Big Data technologies and methodologies. Data is generated constantly in an ever growing number of places and by an ever growing number of actors while a large proportion of potentially re-usable data resides within silos within institutions or companies. These are needed when conventional database technologies cannot be applied to storage and computing issues. The issue of big data has been referred to as the next frontier in computing. In this paper, we research on factors to design an organizational value chain for Big Data.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.6
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• pp.249-267
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• 2021

The use of big data accumulated along with the progress of digitalization is bringing disruptive innovation to the global agricultural industry. Recently, the government is establishing an agricultural big data platform and a support organization. However, in the domestic agricultural industry, the use of big data is insufficient except for some companies in the field of cultivation and growth. In this context, this study identifies factors affecting the intention to use big data in terms of technology, organization and environment, and also confirm the moderating effect of technical field, focusing on agricultural ventures which should be the main entities in creating innovation by using big data. Research data was obtained from 309 agricultural ventures supported by the A+ Center of FACT(Foundation of AgTech Commercialization and Transfer), and was analyzed using IBM SPSS 22.0. As a result, Among technical factors, relative advantage and compatibility were found to have a significant positive (+) effect. Among organizational factors, it was found that management support had a positive (+) effect and cost had a negative (-) effect. Among environmental factors, policy support were found to have a positive (+) effect. As a result of the verification of the moderating effect of technology field, it was found that firms other than cultivation had a moderating effect that alleviated the relationship between all variables other than relative advantage, compatibility, and competitor pressure and the intention to use big data. These results suggest the following implications. First, it is necessary to select a core business that will provide opportunities to generate new profits and improve operational efficiency to agricultural ventures through the use of big data, and to increase collaboration opportunities through policy. Second, it is necessary to provide a big data analysis solution that can overcome the difficulties of analysis due to the characteristics of the agricultural industry. Third, in small organizations such as agricultural ventures, the will of the top management to reorganize the organizational culture should be preceded by a high level of understanding on the use of big data. Fourth, it is important to discover and promote successful cases that can be benchmarked at the level of SMEs and venture companies. Fifth, it will be more effective to divide the priorities of core business and support business by agricultural venture technology sector. Finally, the limitations of this study and follow-up research tasks are presented.

• Journal of Fashion Business
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• v.22 no.6
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• pp.83-93
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• 2018

The purpose of this research is to investigate the characteristics of big data-based fashion shopping (BDFS) application, perceived usefulness, and expectation confirmation that influence the continuous usage intention of BDFS application users based on the expectation-confirmation model. A survey was conducted with female consumers in their 20s, who are living in Seoul and Incheon area and have used BDFS applications, A total of 182 responses were used for the data analysis. Five hypotheses were proposed, and regression analyses were conducted to test those hypotheses. The results indicated that the users' perceived usefulness increased with the increase of accuracy and personalization characteristics of the app and the expectation confirmation. The result suggested that it is essential to provide accurate information for users to feel useful and to develop the personalized offerings and services which can be the biggest strength of the big-data based mobile fashion store. It was also found that continuous usage intention increases with increased perceived usefulness and expectation confirmation. This result suggests that expectations can play a critical role in perceiving the usefulness of BDFS applications and the user's expectation confirmation also significantly affected the users' continuous usage intention.

• Knowledge Management Research
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• v.16 no.4
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• pp.69-87
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• 2015

Given the huge number of data produced by its users, SNS is a great source of customer insights. Since viral trends in SNS reflect customers' direct feedback, companies can draw out highly meaningful business insights when such data is effectively analyzed and managed. However, while the importance of understanding SNS big data keeps growing, the methods for analyzing atypical data such as SNS postings for business insights over product has not been well studied. This study aims to demonstrate the way to exploit topic modeling method to support marketing strategy generation and therefore leverage business process. First, we conducted topic modeling analysis for twitter data of Apple and Samsung smartphones. Then we comparatively examined the analysis results to draw meaningful market insights about each smartphone product. Finally, we draw out a strategic marketing recommendation for each smartphone brand based on the findings.

• Asia pacific journal of information systems
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• v.26 no.1
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• pp.66-79
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• 2016

The availability of detailed data on customers' online behaviors and advances in big data analysis techniques enable us to predict consumer behaviors. In the past, researchers have built purchase prediction models by analyzing clickstream data; however, these clickstream-based prediction models have had several limitations. In this study, we propose a new method for purchase prediction that combines information theory with machine learning techniques. Clickstreams from 5,000 panel members and data on their purchases of electronics, fashion, and cosmetics products were analyzed. Clickstreams were summarized using the 'entropy' concept from information theory, while 'random forests' method was applied to build prediction models. The results show that prediction accuracy of this new method ranges from 0.56 to 0.83, which is a significant improvement over values for clickstream-based prediction models presented in the past. The results indicate further that consumers' information search behaviors differ significantly across product categories.

• Journal of Agricultural Extension & Community Development
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• v.17 no.3
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• pp.475-504
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• 2010

This study focuses on the discovering problems which have been encountered while the farm company grew from one-man business to big company. We divide a growth process of farm company into 7 phases, and analyze the problems of each phase of growth process of farm company by theoretically and empirically. The logical model methodology is selected to test the basic model, and we use 6 cases to test suggested basic model. The results of this study are as follows. It suggests that the CEO of farm enterprise or its policy maker can understand what kinds of problem they meet while the farm enterprise grew from one-man business to big company, and they can make better strategies or better support policies.