디지털 전환(Digital Transformation) 시기에 인사 데이터를 활용한 전략적 인사 의사결정이 중요해졌 으며 이를 지원할 People Analytics(PA)에 대한 수요와 관심이 증가하고 있다. 본 논문은 PA를 수행하기 위해 필요한 역량이 무엇인지 텍스트 마이닝과 선행 연구를 통해 알아본다. 선행 연구에서는 반구조화 인터뷰와 문헌 연구를 통해 연역적 연구 방법으로 PA 필요 역량을 제시한 반면 본 연구에서는 텍스트마 이닝 기법을 적용하여 귀납적 방법을 활용했다. 이를 통해 선행 연구를 보완함으로써 일반화 가능성을 높이고자 했다. 링크드인(LinkedIn) PA 채용공고 데이터를 활용해 분석한 결과, 주요 역량이 다섯 가지가 도출되었다. 첫째, 비즈니스 및 인적 통찰력(Business and People Acumen), 둘째, 데이터 분석(Data Analysis), 셋째, 의사소통(Communication), 넷째, 문제 해결력(Problem Solving), 다섯째, 상호 작용 (Interpersonal)까지 다섯 가지 PA 필요 역량이다. 다섯 가지 역량은 기존 연역적 분석으로 도출된 결과를 지지 및 보완한다고 할 수 있다. 이를 통해 기존 연구에 방법론적인 새로움을 더한다고 할 수 있고, 동시 에 실무 측면에서는 채용과 육성 관점에서 어떤 역량을 주요하게 봐야 하는지를 제시했다고 볼 수 있다.

현대 해양 산업은 기술적 발전을 통해 신속한 발전을 이루고 있다. 이러한 발전을 주도하는 주요 기술 중 하나는 데이터 처리 기술이며, 이 중 자연어 처리 기법은 사람의 언어를 기계가 이해하고 처리할 수 있도록 하는 기술이다. 본 연구는 자연어 처리 기법을 통해 해양안전심판원의 재결서를 분석하여 이미 재결이 이루어진 선박 충돌사고의 원인 제공 비율을 학습한 후, 새로운 재결서를 입력 하면 원인 제공 비율을 예측하는 모델을 개발하고자 하였다. 이 모델은 사고 당시 적용되는 항법과 원인 제공 비율에 영향을 주는 핵심 키워드의 가중치를 이용하여 사고의 원인 제공 비율을 계산하는 방식으로 구성하였다. 이 연구는 이러한 방식을 통해 제작한 모델의 정 확도를 분석하고, 모델의 실무 적용 가능성을 검토함과 동시에 충돌사고 재발 방지 및 해양사고 당사자들의 분쟁 해결에 기여할 것으로 기대한다.

This study explores the effectiveness of marketing data analytics learning and outcomes for marketing students in courses with data analysis components at a U.S. business school. The study considers various moderating factors, such as software adaptability, grades, class type, data interest, statistical analysis method, and perceived time- and cost-effectiveness. The findings have implications for marketing education in data analytics.

This study explored dominant topics about the metaverse discussed in Twitter and the sentiments in each topic in the case of Decentraland using topic modeling and sentiment analysis. The appraisal theory of emotion and motivation theory were used to explain why positive or negative sentiments were expressed toward specific topics. The majority of topics were related to economic benefits such as coins, NFTs, tokens, estate, land, and spaces or socializing with others at specific events. Many of them included predominantly positive sentiments because consumers appraised them as motive consistent. This serves as an important implication for marketers and developers in the metaverse that they need to focus more on these features so that consumers can interact with the motive-consistent features and thus have positive emotions.

While there is an increasing number of studies highlighting the power of videos in influencing audience attitudes and behavior, academic research in tourism is largely behind due to the methodological challenges of analyzing unstructured video data. This study adopts an automatic video analytics approach to examine the relationship between content features of pro-environmental videos and audience engagement in tourism. Artificial intelligence was used to extract video content features by detecting scenes and shots as well as labels (e.g., trees). Our findings suggest that there exists an inverted U-shape relationship between video informativeness and audience engagement. This study makes significant theoretical and methodological contributions to extant tourism literature by theoretically explaining and empirically testing how video content features influence audience engagement in pro-environmental video communications in tourism.

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.

This study investigates the contexts in which emojis occur through co-occurrence, cluster, and association analysis of Airbnb tweets. Findings reveal these positive emojis tend to co-exist with several types of words representing conversation, emotion, activity, and marketing. This study contributes to the textual paralanguage literature in marketing.

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.

This research intends to propose the methodology for analyzing the current trends of agriculture, which directly connects to the survival of the nation, and through this methodology, identify the agricultural trend of Korea. Based on the relationship between three types of data – policy reports, academic articles, and news articles – the research deducts the major issues stored by each data through LDA, the representative topic modeling method. By comparing and analyzing the LDA results deducted from each data source, this study intends to identify the implications regarding the current agricultural trends of Korea. This methodology can be utilized in analyzing industrial trends other than agricultural ones. To go on further, it can also be used as a basic resource for contemplation on potential areas in the future through insight on the current situation. database of the profitability of a total of 180 crop types by analyzing Rural Development Administration’s survey of agricultural products income of 115 crop types, small land profitability index survey of 53 crop types, and Statistics Korea’s survey of production costs of 12 crop types. Furthermore, this research presents the result and developmental process of a web-based crop introduction decision support system that provides overseas cases of new crop introduction support programs, as well as databases of outstanding business success cases of each crop type researched by agricultural institutions.

Over the past decade, the advances in the Internet of Things has allowed WiFi infrastructure to track the movement and location of smart devices. This innovative technology is sometimes referred to as wireless analytics or offline / in-store visitor analytics. Similar to an offline or instore version of website analytics, wireless analytics can infer instore shopping behavior from analyzing the dwell time, movement, and behavior of a smart device within a designated vicinity. The study was carried out at an activation area of food trucks at an Australian metropolitan university. Visitor analytics were gathered by using a wireless analytic modem that was configured to ping and pick up wireless signal emitted by smart devices within the radius of the food truck area. Challenging past research on pop-stores, our findings show that novelty of pop-up food trucks may not necessarily predict their success and consumers tend to prefer familiar food trucks at the Australian metropolitan university. In fact, the presence of novel food trucks may encourage consumers to walk-by without any interaction with the food trucks.

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 aims to analyse the overall sentiments of online reviews on restaurants in Malaysia using predictive text analytics. As we know in opinion mining, sentiment analysis is a prominent technique in predictive text mining. It is a technique that categorises opinions in unstructured text format into binary classification (ie. good or bad). The authors attempt to go beyond the binary classification by viewing texts as empirical entities derived using the Term Frequency - Inverse Document Frequency (TF-IDF) weighting algorithm. These empirical entities, based on online reviews of restaurants in Malaysia, are then manifested into hypothetically defined constructs closely reflecting their thematic and semantic nature. The were 4914 customer reviews from restaurants across 20 towns and cities in Malaysia scraped off TripAdvisor.com using web crawler tools. Then a series of analytical tests were carried out. First the online reviews were parsed, filtered and clustered using SAS Text Miner. Then the online reviews underwent the TF-IDF process to identify significant terms and weightages were assigned according to their importance. The TF-IDF process resulted in a series of important nouns and adjectives from the text corpus. Using these weightages of nouns and adjectives, the authors went on to thematise these terms based on their semantic nature to manifest hypothetical constructs. These constructs were based on the Mehrabian–Russell Stimulus Response Model. Subsequently the authors tested the associations among the constructs using variance-based and covariance-based Structural Equation Modelling (SEM). The authors were encouraged by this exploratory methodological approach in formulating predictive text analytics using SEM. Results indicated that sentiments were generally positive towards restaurants and the important terms derived were price, hospitality, location, waiting time, availability of parking and size of food portion.

Social media have been proved as a tool for social branding, but not as a tool for return on investment (ROI) generation. The ultimate goal of any business activities is to generate ROI; therefore, businesses should know what social media practices actually increase their ROI. Researchers in the computer science and engineering areas have attempted to create a systematic model/statistical method to quantify data collected from social media to generate meaningful consumer and market trends and ROI (Zeng, Chen, Lusch, & Li. 2010). This process is called Social Media Intelligence (SMI) or Social Media Analytics (SMA). Researchers have not been yet successful in developing an effective analytical system for social media data to generate ROI. Therefore, the purpose of this study is to explore which social media practices would affect ROI based on SMA process with key techniques used to analyze the indicators in social media (i.e., Key Performance Indicators; KPIs) that show the effectiveness of a company in achieving its business objectives. This study is an exploratory research to determine the nature of a problem in the SMI, to gain further insight, and to show opportunities in the subject area. The result shows that using crawling, topic modeling and social network analysis techniques, businesses could collect and monitor right KPIs depending on their social media goals (e.g., number of followers for awareness, number of link clicks for engagement, number of lead magnets for conversion). After then, using the techniques to analyze the KPIs (e.g., opinion mining, sentiment analysis, etc. for the understand stage), businesses would be able to identify/predict consumer demands and market trends. Based on this prediction, businesses need to visualize the result to customers by executing right marketing strategies (e.g., effective viral marketing, personalized Call-To-Action, customized product/service, direct relationship establishment, frequent communication, establish long relationship, etc.). This study could contribute to the field by presenting the effective KPIs and techniques organized based on the SMA stages and social media goals and could provide the industry a right tool and a direction for their social media promotional practices.

The mortality rate in industrial accidents in South Korea was 11 per 100,000 workers in 2015. It’s five times higher than the OECD average. Economic losses due to industrial accidents continue to grow, reaching 19 trillion won much more than natural disaster losses equivalent to 1.1 trillion won. It requires fundamental changes according to industrial safety management. In this study, We classified the risk of accidents in industrial complex of Ulju-gun using spatial analytics and data mining. We collected 119 data on accident data, factory characteristics data, company information such as sales amount, capital stock, building information, weather information, official land price, etc. Through the pre-processing and data convergence process, the analysis dataset was constructed. Then we conducted geographically weighted regression with spatial factors affecting fire incidents and calculated the risk of fire accidents with analytical model for combining Boosting and CART (Classification and Regression Tree). We drew the main factors that affect the fire accident. The drawn main factors are deterioration of buildings, capital stock, employee number, officially assessed land price and height of building. Finally the predicted accident rates were divided into four class (risk category-alert, hazard, caution, and attention) with Jenks Natural Breaks Classification. It is divided by seeking to minimize each class’s average deviation from the class mean, while maximizing each class’s deviation from the means of the other groups. As the analysis results were also visualized on maps, the danger zone can be intuitively checked. It is judged to be available in different policy decisions for different types, such as those used by different types of risk ratings.