• International Journal of Internet, Broadcasting and Communication
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• 제15권2호
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• pp.39-48
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• 2023

The purpose of this study is to revitalize ESG tourism in Bhutan by comparing and analyzing the ESG tourism status in Bhutan and the ESG tourism status in Korea. Big data analysis using text mining was performed by selecting "Bhutan ESG Tourism" and "Korea ESG Tourism" as keywords. The top 30 keywords were extracted through word purification, and based on this, data visualization was conducted through network analysis and Concor analysis between each keyword. As a result of the analysis, it was confirmed that Bhutan, unlike Korea, did not utilize it even though it had elements to incorporate ESG and the tourism industry into the country itself. As a result, since it is necessary to combine ESG elements owned by Bhutan and combine them with the tourism industry, we would like to suggest the direction of combining ESG and the tourism industry through this study.

• 대한안전경영과학회지
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• 제14권4호
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• pp.59-70
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• 2012

This study compared data regarding industrial accidents and awareness level for beauty product safety for four main parts of beauty industry such as hair-care, nail-care, skin-care, and make-up. Major risk factors in beauty industry are dust, musculoskeletal disorders, and organic solvent of beauty product in order of percentage. The specific types of industrial accidents in beauty industry are mainly associated with musculoskeletal system such as cuts, sprain, and varicose vein. They are mainly compensated by personal budget. The awareness levels of chemical and heavy metal containment for beauty product by beauty industry employee were 77.2% and 59.1% respectively. Most employee confirmed only important items of labelling requirement of beauty product. Also, most employee did not understand MSDS(Materila Safety Data) for chemicals used in beauty industry. Only 38.1% of beauty industry employee has had safety education while most employee (73.6%) realized that they needed safety education. Also, safety education supervised by KOSHA(Korea Occupational Safety and Health Agency) was the most preferred. This study would be good basis for safe and healthy working environment of beauty industry employee.

• 산경연구논집
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• 제14권3호
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• pp.27-43
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• 2023

Purpose: Changes in the employment environment in the era of the 4th Industrial Revolution are influencing various factors by the emergence of new jobs and the change in perception of job stability due to globalization of information technology and industry This study attempted to present implications by verifying the structural relationship of beauty workers' sustainable organizational commitment and the method necessary for conflict management in the industrial field due to the recent changes in the employment environment of the beauty industry in the 4th Industrial Revolution. Research design, data and methodology: This study sampled 604 beauty industry employees Frequency analysis, validity and reliability analysis, factor analysis, and path analysis were performed using SPSS WIN23.0. Results: It was found that the change in the employment environment caused by the 4th industrial revolution had a significant negative (-) effect on the job satisfaction and organizational commitment of beauty industry workers. Conclusion: This study is that changes in the employment environment negatively affect job satisfaction and organizational commitment of beauty workers. We hope to contribute to the development and growth of the beauty industry by providing basic data for the beauty tech service industry in the 4th industrial era.

• 한국융합학회논문지
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• 제12권2호
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• pp.259-269
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• 2021

국내 지역 산업생태계의 활성화를 위하여 많은 연구자들이 그에 대한 정책수립 등에 대한 논의를 진행하고 있다. 하지만, 각 지역 특성을 고려한 산업생태계 활성화 방안과 그에 대한 우선순위를 고려하는 경우는 드물다. 따라서, 본 연구에서는 광주 지역 자동차 산업을 중심으로 이에 대한 활성화 방안을 도출하기 위하여 문헌조사와 심층 인터뷰를 통하여 전문가 집단의 견해를 모으고 분석하는 정성적 연구를 수행하였다. 일차적으로 문헌조사와 심층 인터뷰를 통해 현재 광주 지역 자동차 산업의 현황 진단과 더불어 어떤 위기가 있는지 알아보고 이를 해결하기 위한 활성화 방안의 11가지의 전략 후보를 도출하였다. 그리고 AHP 기법을 활용하여 도출된 방안들의 상대적 중요도와 우선순위를 알아봄으로써, 어떤 방안부터 우선적으로 적용해야하는지에 대한 5가지 전략을 선별하였으며, 이는 향후 사업 계획 및 전략수립의 기초자료로 활용될 것이다.

• 한국의류학회지
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• 제42권1호
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• pp.148-158
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• 2018

This study analyzes recent trends in fashion retailing instigated by the fourth industrial revolution and approaches the trends in terms of the convergence of big data and artificial intelligence. The findings are as below. First, companies like 'Edited' and 'Stylumia' offer solutions that support the strategic decisions of fashion brands and fashion retailers by analyzing big data using artificial intelligence. Second, the convergence of big data and artificial intelligence scales personalized service on the web as examples of 'Coded Couture', 'StitchFix', and 'Thread'. Third, the insights gained from artificial intelligence and big data help create new fashion retailing platforms such as 'Botshop' and 'Lyst'. Last, artificial intelligence and big data assist with design. 'Ivyrevel' designs digital fashion, assisted by a macroscopic perspective on fashion trends, market and consumers through the analysis of big data. The Fourth Industrial Revolution brings changes across all industries that will likely accelerate. The fashion industry is also undergoing many changes with advancements in scientific technology. The convergence of big data and artificial intelligence will play a key role in the future of fast-moving industry like fashion, where fickle tastes of consumers are the main drivers.

• 한국컴퓨터정보학회논문지
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• 제11권3호
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• pp.97-105
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• 2006

변화하는 기업 환경에 효과적으로 대응하기 위해서는 우리나라 중견 제조업 환경에 적합한 생산 마스터 정보관리 솔루션 도입이 시급하다 따라서 본 논문에서는 사용하기 편리한 사용자 인터페이스를 가지며, 다양한 시스템들과도 쉽게 연동할 수 있도록 표준 데이터 구조를 설계하였다. 또한 표준 데이터 구조를 중간언어 형태로 생성해 주는 자동화 연동 모듈을 탑재하고, 생산 관리 데이터의 확장이 가능한 생산 마스터 정보관리 솔루션을 개발하였다. 본 개발을 통하여 업무적으로는 관리하지 않았던 데이터를 관리함으로써 보다 효율적인 업무 프로세스 구성을 이룰 수 있을 뿐만 아니라 시스템 확장 시 보다 안정적인 시스템 구축을 위한 근간을 제공할 수 있다.

• 한국정보시스템학회지:정보시스템연구
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• 제30권3호
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• pp.159-175
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• 2021

Purpose The purpose of this study was to find the effect of the determinants on the Big data utilization in industry. The determinants of Big data utilization were deduced by reviewing theoretical background and discussions on Big data related researches. Research model and proposed hypothesis were constructed from TOE framework and UTAUT model. Design/methodology/approach The research was conducted to collect a sample data from the experts involved in the Big data projects in industry. In addition, interviews and online survey were performed to get sample data. Exploratory factor analysis was conducted to verify the grouping of these questionnaire items and confirmatory factor analysis was done to verify the validity and reliability of the measurement model. Finally, research hypothesis was verified and theoretical and practical implications were proposed for further studies. Findings The results show that the technical factor have a significant effect on the expectancy factor and the behavioral factor. The organizational factor have a significant effect on the behavioral factor. In addition, the expectancy factor was significant on the behavioral factor and the intention-to-use of Big data system.

• International Journal of Naval Architecture and Ocean Engineering
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• 제12권1호
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• pp.428-439
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• 2020

The continuous development of information and communication technologies has resulted in an exponential increase in data. Consequently, technologies related to data analysis are growing in importance. The shipbuilding industry has high production uncertainty and variability, which has created an urgent need for data analysis techniques, such as machine learning. In particular, the industry cannot effectively respond to changes in the production-related standard time information systems, such as the basic cycle time and lead time. Improvement measures are necessary to enable the industry to respond swiftly to changes in the production environment. In this study, the lead times for fabrication, assembly of ship block, spool fabrication and painting were predicted using machine learning technology to propose a new management method for the process lead time using a master data system for the time element in the production data. Data preprocessing was performed in various ways using R and Python, which are open source programming languages, and process variables were selected considering their relationships with the lead time through correlation analysis and analysis of variables. Various machine learning, deep learning, and ensemble learning algorithms were applied to create the lead time prediction models. In addition, the applicability of the proposed machine learning methodology to standard work hour prediction was verified by evaluating the prediction models using the evaluation criteria, such as the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Logarithmic Error (RMSLE).

• 한국IT서비스학회지
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• 제21권1호
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• pp.145-161
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• 2022

The data is a key catalyst for the development of the fourth industry, and has been viewed as an essential element of the new industry, with technology convergence such as artificial intelligence, augmented/virtual reality, self-driving and 5 G. This will determine the price and value of the data as the user uses data in which the data is based on the context of the situation, rather than the data itself of the past supplier-centric data. This study began with, what factors will increase the value of data from a user perspective not a supplier perspective The study was limited to public data and users conducted research on users using data, such as analysis or development based on data. The study was designed to gauge the value of data that was not studied in the user's perspective, and was instrumental in raising the value of data in the jurisdiction of supplying and managing data.

• 한국조리학회지
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• 제23권7호
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• pp.1-10
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• 2017

This study proposed SNS big data analysis method of food service industry in the 4th industrial revolution. This study analyzed the keyword of the fourth industrial revolution by using Google trend. Based on the data posted on the SNS from January 1, 2016 to September 5, 2017 (1 year and 8 months) utilizing the "Social Metrics". Through the social insights, the related words related to cooking were analyzed and visualized about attributes, products, hobbies and leisure. As a result of the analysis, keywords were found such as cooking, entrepreneurship, franchise, restaurant, job search, Twitter, family, friends, menu, reaction, video, etc. As a theoretical implication of this study, we proposed how to utilize big data produced from various online materials for research on restaurant business, interpret atypical data as meaningful data and suggest the basic direction of field application. In order to utilize positioning of customers of restaurant companies in the future, this study suggests more detailed and in-depth consumer sentiment as a basic resource for marketing data development through various menu development and customers' perception change. In addition, this study provides marketing implications for the foodservice industry and how to use big data for the cooking industry in preparation for the fourth industrial revolution.