본 연구에서는 미용분야 창업 활성화를 위해 소셜 빅데이터 분석을 탐색적 데이터 분석(EDA) 을 기반으로 하여 2019년부터 2021년 동안 각 년도별로 기간을 구분하여 ‘미용창업’에 대한 수요 변화와 감정 및 의미 차이의 특징적인 패턴을 도출하고자 하였다. ‘미용창업’ 키워드를 주제로 연관된 검색어를 추 출한 결과 창업에 필요한 전문적인 창업교육 보다는 미용관련 기술을 배울 수 있는 기관이나 자격증에 더 많은 관심을 보였으며, 이는 정부 및 지자체에서 여러 가지 창업지원 정책들이 마련되고 있음에도 불구하 고 여전히 전문적인 창업교육의 중요성을 인식하지 못하고 있는 것으로 파악할 수 있으며, 이에 대한 대안 으로 미용분야 창업을 성공적으로 이루기 위한 전공별 맞춤형 창업교육 프로그램을 개발하는 것이 필요할 것으로 사료된다. 탐색적 데이터 분석을 통해 가설을 설정하고 전통적인 확증적 데이터 분석(CDA)을 결합 하여 가설을 검증한다. 미용 창업을 위한 탐색적 데이터 분석 방법이 존재한 적은 없으며, 정식 창업교육의 필요성을 언급하기보다는 미용창업에 대한 관심 변화와 예비창업자의 요구사항을 탐색적 데이터로 분석한 다면 맞춤형 창업 프로그램 개발에 도움이 될 것이라고 확신한다.

This study investigated the impact of the latest developments in big data analytics capabilities (BDAC) on firm performance. The BDAC have the power to innovate existing management practices. Nevertheless, their impact on firm performance has not been fully is not yet fully elucidated. The BDAC relates to the flexibility of infrastructure as well as the skills of management and firm's personnel. Most studies have explored the phenomena from a theoretical perspective or based on factors such as organizational characteristics. However, this study extends the flow of previous research by proposing and testing a model which examines whether organizational exploration, exploitation and market agility mediate the relationship between the BDAC and firm performance. The proposed model was tested using survey data collected from the long-term employees over 10 years in 250 companies. The results analyzed through structural equation modeling show that a strong BDAC can help improve firm performance. An organization's ability to analyze big data affects its exploration and exploitation thereby affecting market agility, and, consequently, firm performance. These results also confirm the powerful mediating role of exploration, exploitation, and market agility in improving insights into big data utilization and improving firm performance.

우리나라에서 빅데이터 분석 기술과 특허 소송에서 활용 가능한 지식재산 서비스 또는 법률 서비스 사이의 격차를 줄이기 위한 방안을 제안하였다. 지식재산 서비스에서 국내 기업은 해외 기업과 비교하여 데이터의 양과 질적인 측면에서 열세에 있으므로 국내의 지식재산 서비스를 제공하는 기업들은 데이터에 대한 정보처리 기술을 고도화하여 경쟁력을 확보할 필요가 있다. 또한 특허 빅데이터 분석 기법에 관한 다양한 연구를 활용할 수 있는 알고리즘과 데이터를 오픈소스 방식으로 공개할 수 있는 플랫폼을 제공하여 연구결과의 활용성을 강화할 필요가 있다. 지식재산 서비스의 활용도를 높이기 위해서는 데이터와 알고리즘에 대한 객관성과 투명성이 확보되어야 할 것이다. 법률 서비스에서는 법률산업 선진화와 신성장 동력 발굴 차원에서 리걸테크 산업을 육성하기 위한 산업투자 및 기반 확충에 나서야 한다. 또한 적정한 수준의 비식별화가 이루어진 판결문에 대해서는 연구자들이 손쉽게 접근할 수 있는 데이터베이스 구축이 필요하다. 우리나라는 영미법과 달리 디스커버리 제도가 존재하지 않고 기본적으로 성문법주의를 채택하고 있는 점을 감안하여 판결문 검색을 위한 공공 서비스 등 우리나라 제도에 필요한 부분에 집중할 필요가 있다. 현행 변호사법에 따라 비변호사가 빅데이터 분석 기술을 활용하여 유상으로 고객들을 상대로 법률사무를 취급하는 것은 금지된다. 그러나 변호사법 제109조 제1호의 궁극적인 목적이 일반 국민을 보호하기 위한 것임을 고려했을 때 적어도 알고리즘의 투명성과 데이터의 객관성이 확보되는 것을 전제로 변호사가 아닌 개인 또는 기업이 빅데이터 분석 기술을 활용해 법률 서비스를 제공하는 것을 일부 허용할 필요가 있으며 변리사 법에 의한 규제도 마찬가지의 고민이 필요하다.

Big data analysis in healthcare research seems to be a necessary strategy for the convergence of sports science and technology in the era of the Fourth Industrial Revolution. The purpose of this study is to provide the basic review to secure the diversity of big data and healthcare convergence by discussing the concept, analysis method, and application examples of big data and by exploring the application. Text mining, data mining, opinion mining, process mining, cluster analysis, and social network analysis is currently used. Identifying high-risk factor for a certain condition, determining specific health determinants for diseases, monitoring bio signals, predicting diseases, providing training and treatments, and analyzing healthcare measurements would be possible via big data analysis. As a further work, the big data characteristics provide very appropriate basis to use promising software platforms for development of applications that can handle big data in healthcare and even more in sports science.

Lately, there have been tremendous shifts in the business technology landscape. Advances in cloud technology and mobile applications have enabled businesses and IT users to interact in entirely new ways. One of the most rapidly growing technologies in this sphere is business intelligence, and associated concepts such as big data and data mining. BI is the collection of systems and products that have been implemented in various business practices, but not the information derived from the systems and products. On the other hand, big data has come to mean various things to different people. When comparing big data vs business intelligence, some people use the term big data when referring to the size of data, while others use the term in reference to specific approaches to analytics. As the volume of data grows, businesses will also ask more questions to better understand the data analytics process. As a result, the analysis team will have to keep up with the rising demands on the infrastructure that supports analytics applications brought by these additional requirements. It’s also a good way to ascertain if we have built a valuable analysis system. Thus, Business Intelligence and Big Data technology can be adapted to the business’ changing requirements, if they prove to be highly valuable to business environment.

Functional magnetic resonance imaging (fMRI) is one of the best available devices that can record the activities of living human brain non-invasively. Its precision and high spatial resolution is matched by none other methodology. The entry barrier to fMRI research is exceptionally high. fMRI has widely been used in medical and scientific research, but its application to marketing research has been limited because of two important reasons. First, the cost problem. The MR scanning devices often cost multi-million dollars and using fMRI for marketing research can be costly. Second, analyzing data from fMRI study is another formidable task. fMRI measures the brain’s hemodynamic activities using voxel as a measuring unit; Voxels are often a cubic with 2 to 3 millimeters on one side. Since a typical adult brain represents over one million voxels in one scan volume, and each scan generally has 2 to 3 seconds of interval time, one experimental block of 40 seconds, for example, will create over 40 million data points. Compared to a typical marketing research data which in general have two dimensions (2d) of rows and columns, fMRI data is inherently 4d with added dimensions of voxel and time. Furthermore, the fMRI signal is sensitive to various sources of noises. In this talk, we offer support for marketing researchers who want to explore fMRI method for their research in the future. First, we discuss issues related to experimental design for fMRI experiments. We explain preprocessing steps that are recommended for fMRI data and show how to apply statistical methods to make inferences that can increase internal validity. Then, we will explicate how to apply big data analytics to fMRI data during this talk to find deep insights into customer’s brains. A real neuromarketing fMRI data will be used to break down the steps for fMRI research and data analytics. Finally, we will open a discussion to discover future research opportunities for marketing research using fMRI. The purpose of this talk is to lower the entry barrier of fMRI method in neuromarketing research so that more people in the marketing field can benefit from the most advanced scientific achievement of our time and discover deepest insights into our customers.

This study analyzes the key drivers (commitment, integration of big data, green supply chain management, and green human resource practices) of sustainable capabilities and the influence to which these sustainable capabilities impact the banks’ environmental and financial performance. Additionally, this study analyzes the impact of green management practices on the integration of big data technology with operations. The theory of dynamic capability was deployed to propose and empirically test the conceptual model. Data was collected through a self-administrated survey questionnaire from 319 participants employed at 35 banks located in six ASEAN countries. The findings indicate that big data analytics strategies have an impact on internal processes and banks’ sustainable and financial performance. This study indicates that banks committed towards proper data monitoring of its clients achieve operational efficiency and sustainability goals. Moreover, our results confirm that banks practising green innovation strategies experience better environmental and economic performance as the employees of these banks have received advance green human resource training. Finally, our study found that internal and external green supply chain management practices have a positive impact on banks’ environmental and financial performance, which confirms that ASEAN banks contributing in reduction of environmental impact through its operations will ultimately experience increased financial performance.

With the recent global urban issues such as climate change, urbanization, and energy problems, the smart city was proposed as one of the solutions in urban planning. This study introduces the smart city initiatives of South Korea by examining the recent history of smart city policies and their limitations. This case study reflects the experience of one of the countries which thrived to building smart cities as their national key industries to drive economic growth. It also analyzes the trends of the smart city using big data analysis techniques. Although there are obstacles such as economic recession, failing to differentiate from the U-city, low service level than expected smart functionality, We could recognize the current status of the smart city policies in South Korea such as 1) Korean smart city development projects are actively implemented, 2) public consensus suggests that applying advanced technology and the active role of government need, 3) a comprehensive and strategic approach with the integration and application of advanced technologies is required as well, 4) investment by both private and public sectors need to deliver social improvements. This study suggests future direction of smart city polity in South Korea in the conclusion.

This study is to explore the relationship between the Fourth Industrial Revolution and the environment using the big data methodology. We scrutinize the trend of the Fourth Industrial revolution, in association with the environment, and provide implications for a more desirable future environmental policy. The results show that the Industrial Revolution has been generally perceived as negative to environment before the 2010s, while it has been widely regarded as positive after the period. It is highly expected that the Fourth Industrial Revolution will be capable of functioning as a new alternative to enhance the quality of the biophysical and social environment. This study justifies that the new wave of technological development may serve as a cure for the enhancement of the environmental quality. The positive linkage between the new technological development and the environment from this study clearly indicates that the environmental industry and environmental technologies will be key economic factors in the next-generation society. They should be of critical importance in shaping our cities into clearer and greener spaces, and people will continuously depend on the development of new environmental technologies in order to correct environmental damages.