빅데이터는 2010년 이후 다양한 산업 분야에서 빠르게 확산이 진행되었다. 본 연구에서는 빅데이터가 확산되는 초기 과정에 대한 시계열 분석을 통해 빅데이터의 범용 기술 특징을 분석하였고, 각 산업의 확산 특성 차이에 대해 조사하였다. 빅데이터를 키워드로 하여 논문, 특허, 뉴스 데이터, 구글트렌드를 분석하여 선행 지수에 해당하는 데이터를 탐색하였고, 논문과 특허보다 뉴스와 구글트렌드가 2년가량 선행하는 트렌드를 보임을 확인하였다. 구글트렌드를 이용하여 국내와 미국, 일본, 중국의 국가별 도입 시기와 확산 양산을 비교하였고, 뉴스 데이터를 통해 국내의 주요한 8가지 산업 분야에 대해 확산이 진행되는 과정을 정량적 그리고 사례를 바탕으로 분석하였다. 본 연구를 통해 빅데이터처럼 산업 전반에 걸쳐 영향을 주는 범용 기술이 어떻게 초기 확산이 이루어지는지에 대한 실증적 연구 방법을 제시하였고, 빅데이터가 국내에서 각 산업별 확산 속도 차이는 어디에서 비롯되는지 파악하였다. 본 논문에서 제시한 방법은 빅데이터 이외에 다른 기술의 확산 과정에도 분석할 수 있으며, 특정 국가내의 기술 키워드 확산에 해당하므로 개발도상국에서 외국으로부터 도입된 기술을 어떻게 받아들일지 분석하는데 사용 가능하다. 그리고, 기업 측면에서는 새로운 기술을 출시하고 이를 확산하고자 할 때 어떤 경로가 효과적인지 이해할 수 있다.

This study conducted to predict the stock market prices based on the assumption that internet news articles might have an impact and effect on the rise and fall of stock market prices. The internet news articles were tested to evaluate the accuracy by comparing predicted values of the actual stock index and the forecasting models of the companies. This paper collected stock news from the internet, and analyzed and identified the relationship with the stock price index. Since the internet news contents consist mainly of unstructured texts, this study used text mining technique and multiple regression analysis technique to analyze news articles. A company H as a representative automobile manufacturing company was selected, and prediction models for the stock price index of company H was presented. Thus two prediction models for forecasting the upturn and decline of H stock index is derived and presented. Among the two prediction models, the error value of the prediction model ① is low, and so the prediction performance of the model ① is relatively better than that of the prediction model ②. As the further research, if the contents of this study are supplemented by real artificial intelligent investment decision system and applied to real investment, more practical research results will be able to be developed.

The purpose of this study is to derive landscape image characteristics of “Insadong-gil” using text mining analysis of big data. Keywords were collected and analyzed through blogs and cafes containing “Insadong-gil” on domestic portal sites Naver and Daum as basic data for research purposes. As a result, 28 landscape image characteristic keywords related to “Insadong-gil” were derived, and the correlation of the extracted keywords was examined to analyze landscape image characteristics. The results of the landscape image characteristics of Insadong-gil are as follows: First, Insadong-gil is recognized as a street of diverse cultures, including “traditional culture” and a “clean street” reputation. Second, we see that “couples” and “tourists” visit for activities such as a “picnic”, “date”, or “travel”. Couples are able to take “photos” and foreign tourists visit to enjoy the hanbok experience on Insadong-gil. Third, the purpose of visiting Insadong-gil includes keywords for activities such as “picnic”, and “walking”, but cultural activities such as exhibitions, performances, and experiences appeared less. Therefore, it is deemed necessary to continuously plan and implement marketplaces, festivals, and events on Insadong-gil as supplements; and create a cultural space visitors can actively approach with familiarity.

최근 대한민국에서는 사회·경제적으로 지위가 있는 사람들의 비윤리적 행위인 ‘갑 질’이 사회적 문제로 대두되고 있다. 사회적 지위를 가진 사람이 비도덕적 행위 혹은 범죄를 저지를 때마다 미디어에 ‘갑질’이라는 용어가 빈도 높게 사용되고 있다. 이에 본 연구에서는 ‘갑질’ 행위를 범죄학적 관점(화이트칼라 범죄이론)과 개인 성격적 관점(화이트칼라 사이코패스이론)으로 비교하여 분석해보고자 한다. ‘갑질’을 범죄학적 관점으로 설명하면 ‘갑질’의 행위자는 대부분 사회적으로 지위가 높은 개인 또는 집단 이라는 점에서 사회적 위치가 중요요인인 화이트칼라 범죄와 부분적으로 일치한다. 개인 성격적인 관점인 화이트칼라 사이코패스이론을 통해 ‘갑질’을 해석하면 ‘갑질’ 행위자의 사회적 지위를 배제하고 개인의 성격특질인 충동성, 폭발적 분노 등을 ‘갑질’의 원인으로 보아 ‘갑질’ 행위자가 화이트칼라 사이코패스의 성격특질을 지녔다고 볼 수 있다. 이를 위해 빅데이터 분석을 사용해 2013년부터 2018년까지의 네이버 뉴 스기사와 소셜미디어의 한 종류인 Twitter 기록의 문자파일을 수집하여 각 파일 안에서 사용된 특정 단어와 중복되어 사용된 단어들의 빈도를 추적하였다. 연차별로 150개 의 단어를 선발해 상위 50위까지의 단어를 분석한 결과, 사회적 특성을 나타내는 단어들이 개인적 성격특질을 나타내는 단어들보다 많은 수를 보였다. 이를 통해 대중의 ‘갑 질’에 대한 인식은 행위자 개인의 성격특성보다 행위자의 직업과 지위 등 사회적 특성 에 더 집중함을 알 수 있었다. 이 결과는 대중들이 ‘갑질’ 사건을 보는 시선은 앞선 두 가지 범죄이론 중 화이트칼라 범죄이론에 더 부합하는 것으로 해석할 수 있다.

Digitalization has generated massive amounts of available data sources (Wedel and Kannan 2016). Consequently, firms aim to exploit this additional value – particularly in decision-making (Barton and Court 2012). However, potential misleading consequences of Big Data for companies have not been examined yet – neither in practice nor in research. Addressing this research gap, the current investigation first uncovers questionable managerial outcomes and behaviours generated by Big Data. The results of a first paper-and-pencil experiment show that executives tend to rely on Big Data even in a domain where this may be misleading (i.e., innovation) (Martin and Golsby-Smith 2017). Interestingly, this relationship is found to be particularly evident for top-managers. A second online study does not only replicate the findings in a correlational setting but beyond sheds light on its mechanism. We show that Big Data activates top-executives’ promotion focus leading them to become more risk seeking and egocentric. In study 3, we replicate these findings through experimentation and moderation underlining its robustness. Finally, we detect a lever to avoid that Big Data leads to less defensive decision behaviour (study 4).

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.

Technology has turned the consumer into a non-stop generator of traditional, structured, transactional data as well as more contemporary, unstructured, behavioural data. The study of consumer analytics turns on the junction of Big Data and consumer behaviour. This paper intends to test empirically the conceptual model proposed by Erevelles, Fukawa and Swayne (2016) for FB, in Portugal. A qualitative methodology is being performed, namely conducting twenty-five in-deep interviews with the current managers of FB, with the average duration of one hour. Once the 25 interviews are all carried out, they will be subsequently transcribed in full. Then, a content analysis will be held by two researchers and resorted to software NVivo, which is considered a good tool to help in theory building (Azevedo, 1998). Since the study is still at the moment of data collection through in-deep interviews, it is not yet possible to present consistent results. However, the first interviews leads to the idea that FB in Portugal do not have a structure particularly dedicated to identifying business opportunities that favor the implementation of an organized Big Data management system. It was also recognize the option of a more centralized management strategy, in anticipation the changes in the environment that can predict trends in the consumer market. Finally, it was verify that the definition of a particular profile of skills and knowledge associated with the agents involved in the collection and treatment of the inputs given by the consumers through the analysis of the Big Data, is not yet defined.

As visual marketing gains a more critical role in marketing communications, consumer eye-tracking data has been utilized to assess the effectiveness of those marketing efforts (Croll, 2016; Glazer, 2012). With eye-tracking data, researchers can capture consumers’ visual attention effectively and may predict their behavior better than with traditional memory measures (Wedel & Pieters, 2008). However, due to the complexity of data: its volume, velocity and variety, known as 3Vs of Big Data, marketing scholars have been slow in fully utilizing eye-tracking data. These data properties may pose a challenge for researchers to analyze eye-tracking data, especially gaze sequence data, with traditional statistical approaches. Commonly, researchers may analyze gaze sequences by computing average probabilities of gaze transitions from a particular area of interest to another area of interest. When the variance of gaze sequence data in the sample is small, this method would uncover a meaningful “global” trend, a trend consistent across all the individuals. However, when the variance is large, this method may not enable researchers to understand the nature of the variance, or the “messiness” of data. In this paper, first, to overcome this challenge, we propose an innovative method of analyzing gaze sequence data. Utilizing the singular value decomposition, our proposed method enables researchers to reveal a “local” trend, a trend shared by only some individuals in the sample. Second, we illustrate the benefits of our method through analyzing gaze sequence data collected in an advertising study. Finally, we discuss the implications of our proposed method, including its capability of uncovering a hidden “local” trend in “messy” gaze sequence data.

해양사고 감소를 위해 다양한 연구들이 수행되어 왔다. 그에 비해 준해양사고에 대한 연구는 미미한 수준에 그치고 있다.준해 양사고는 건수가 많은 대신 내용이 정성적이기 때문에 분석하기에는 현실적인 어려움이 있었다. 하지만 해양사고 감소를 위해서는 준해 양사고의 정량적인 분석이 필요하다. 이번 논문의 목적은 준해양사고 경향을 예측하고 해양사고를 감소시키기 위해 빅데이터 기법을 적용하여 준해양사고 데이터를 정량적으로 분석하는 것이다. 이를 위해 10,000여건의 준해양사고 보고서를 전처리 작업을 통해 통일된 양식 으로 정리하였다. 전처리된 데이터에 대해서 1차적으로, 텍스트마이닝 기법을 적용하여 항해 중 준해양사고 발생원인에 대한 주요 키워드를 도출하였다. 주요 키워드에 대해 2차로 시계열 및 클러스터 분석을 통해 발생할 수 있는 준해양사고 상황에 대한 경향 예측을 도출 하였다. 이번 연구에서는 정성적 자료인 준해양사고 보고서를 빅데이터 기법을 활용하여 정량화된 데이터로 전환할 수 있고, 이를 통해 통계적 분석이 가능함을 확인하였다. 또한 빅데이터 기법을 통해 차 후 발생할 수 있는 준해양사고에 대한 객관적인 경향을 파악함으로써 예방 대책에 대한 정보 제공이 가능함을 확인할 수 있었다.

The purpose of this study is to analyze the landscape image of the Seoul City Wall, focusing on the external restoration without paying attention to the landscape. To derive the results, data were collected from a domestic portal site and the original text of documents and used as the basic data for the study. As a result, 21 landscape image keywords related to the “Naksan course” were derived, and the correlation of the extracted keywords was analyzed. First, the landscape image of the Naksan section does not mean the characteristics of the place, but rather how it has been perceived as a physical element of the city for about 600 years. Second, the landscape image of the Naksan section can be divided into positive and negative images according to people’s reasons for visiting. Visitors pursuing a positive physical activity had a positive image, while those pursuing a passive activity had a negative image. Third, there is a need for a variety of landscape elements that can bring out the emotions of the visitors, because landscape images are derived in various ways. In this study, the Naksan section was the sole focus, and landscape image studies of the other sections are still ongoing, so visitors need to understand the landscape image needed for the Seoul City Wall and discuss the direction it should go. In addition, technical studies should be conducted to make up for the limitations of the text mining method used here to derive the results.

Current evaluation practices for IT projects suffer from several problems, which include the difficulty of self-explanation for the evaluation results and the improperly scaled scoring system. This study aims to develop a methodology of opinion mining to extract key factors for the causal relationship analysis and to assess the feasibility of quantifying evaluation scores from text comments using opinion mining based on big data analysis. The research has been performed on the domain of publicly procured IT proposal evaluations, which are managed by the National Procurement Service. Around 10,000 sets of comments and evaluation scores have been gathered, most of which are in the form of digital data but some in paper documents. Thus, more refined form of text has been prepared using various tools. From them, keywords for factors and polarity indicators have been extracted, and experts on this domain have selected some of them as the key factors and indicators. Also, those keywords have been grouped into into dimensions. Causal relationship between keyword or dimension factors and evaluation scores were analyzed based on the two research models-a keyword-based model and a dimension-based model, using the correlation analysis and the regression analysis. The results show that keyword factors such as planning, strategy, technology and PM mostly affects the evaluation result and that the keywords are more appropriate forms of factors for causal relationship analysis than the dimensions. Also, it can be asserted from the analysis that evaluation scores can be composed or calculated from the unstructured text comments using opinion mining, when a comprehensive dictionary of polarity for Korean language can be provided. This study may contribute to the area of big data-based evaluation methodology and opinion mining for IT proposal evaluation, leading to a more reliable and effective IT proposal evaluation method.

In recent years, China initiated Big Data strategies and put forward a series of legislative proposals with regard to the regulation and utilization of Big Data technology. However, academics have not reached consensus to fundamental questions such as data ownership and protection approaches yet. The intrinsic contradiction lies in the difference of values between Big Data which emphasizes “open and sharing” and intellectual property law that protects monopoly interests. This article seeks to conceptualize Big Data in a dynamic approach with an aim to frame the dialogue for further discussion. Through analyzing whether current intellectual property laws in China serve a solid base for promoting the development of big data technology, it proposes that, in order to address regulatory impracticality of Big Data, certain statutory amendments are necessary. However, regarding the revolutionized proposition of creating a “database right” or alleging “Big Data as an object of intellectual property law,” this research recommends a modest and restrained approach.

Entering the New-normal era, China has defined the start-up as a driving force for sustainable economic development. China's start-up support policy, represented by "makers", is now China's biggest issue. China has established a society based on makers and presented a new development model that enables socially diverse economic communities to continuously develop the Chinese economy. This paper intends to enhance the structural and in-depth understanding of “makers-based society” by analyzing the socially influential actors/keywords of China's makers policy. For this purpose, Big data related to makers were collected for data analysis and data mining and semantic network analysis were conducted. The results of the analysis are as follows: First, China's makers policies are steadily expanding and developing due to active government support and the enthusiasm of social entrepreneurship; Second, from the social point of view, the value of the start-up, the market orientation, and the regional support policy are important; Third, China has been actively developing and utilizing entrepreneurship-related curriculum, and it has been cultivating human resources suitable for start-up; Fourth, makers are an important platform for globalization of human resources in China as a driving force for sustainable development. It is also revealed that young people are building their own entrepreneurial ecosystem through their start-up and realizing their dreams.

목적 : 본 연구는 상대가치점수를 기반으로 하는 국내 건강보험수가의 행위별 수가제가 국제 기능･장애 및 건강에 대한 분류(International Classification of Functioning, Disability and Health; ICF)의 건강 개 념에 적합한 비용을 지출하고 있는지 알아보고자 하였다. 연구방법 : 2003년-2013년 건강보험 및 의료급여권자 중 인구전체를 대표하는 100만 명의 샘플인 국민 건강보험공단의 건강보험 표본코호트 자료를 이용하였다. 건강보험요양급여비용의 이학요법료 중 제3절 전문재활치료료에 해당하는 행위들을 건강보험심사평가원에서 제시한 행위정의에 따라 신체기능과 활동 및 참여로 분류한 후 청구 통계량을 비교분석하였다. 결과 : 국내 재활치료 수가체계는 독립적인 일상생활활동, 활동/참여 그리고 가정이나 사회로 복귀를 통한 삶의 질 향상이라는 ICF의 건강 및 재활의학의 개념을 반영하지 못하고 있다. 또한, 환자의 상병군, 중 증도에 따른 재활치료의 효율적 수행을 위한 급성기–아급성기(회복기)-만성기의 재활의료체계가 정립되어 있지 않음을 확인하였다. 결론 : 재활치료의 효율적 수행을 위해서는 급성기- 아급성기(회복기)- 만성기의 재활의료체계가 정립되어야 하고 재활의료체계 내에서 의료기관 종별 역할이 구분이 필요하다. 이와 함께 적절한 재활치료 보험수가 체계 그리고 심사기준의 신설 및 개선이 필요하다.