• The Journal of the Korea Contents Association
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• v.10 no.5
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• pp.115-123
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• 2010

Because of development of information technology, moving picture can run various platforms. We should consider and apply users' attitude as well as production technique because convergence between mobile and media technology may be increased full-browsing service using mobile device. Previous research related to production technique in various platforms only focus on video quality and adjustment of screen size. However, besides of technical side, production techniques should be changed such as image production as well as image editing by point of view aesthetic. Mise-en-scene such as camera angle, composition, and lighting is changed due to HD image. Also image production should be changed to a suitable full-browsing service using mobile device. Therefore, we would explore a new suitable production techniques and image editing for smart phone. To propose production techniques for smart phone, we used E-learning production system, which are transition, editing technique for suitable converting system. Such as new attempts are leading to new paradigm and establishing their position by applying characteries such as openness, timeliness to mobile. Also it can be extended individual area and established as expression and play tool.

• Journal of Internet Computing and Services
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• v.17 no.3
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• pp.107-113
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• 2016

Following the recent advancement in the use of social networks, a vast amount of different online reviews is created. These variable online reviews which provide feedback data of contents' are being used as sources of valuable information to both contents' users and providers. With the increasing importance of online reviews, studies on opinion mining which analyzes online reviews to extract opinions or evaluations, attitudes and emotions of the writer have been on the increase. However, previous sentiment analysis techniques of opinion-mining focus only on the classification of reviews into positive or negative classes but does not include detailed information analysis of the user's satisfaction or sentiment grounds. Also, previous designs of the sentiment analysis technique only applied to one content domain that is, either product or movie, and could not be applied to other contents from a different domain. This paper suggests a sentiment analysis technique that can analyze detailed satisfaction of online reviews and extract detailed information of the satisfaction level. The proposed technique can analyze not only one domain of contents but also a variety of contents that are not from the same domain. In addition, we design a system based on Hadoop to process vast amounts of data quickly and efficiently. Through our proposed system, both users and contents' providers will be able to receive feedback information more clearly and in detail. Consequently, potential users who will use the content can make effective decisions and contents' providers can quickly apply the users' responses when developing marketing strategy as opposed to the old methods of using surveys. Moreover, the system is expected to be used practically in various fields that require user comments.

• Journal of Service Research and Studies
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• v.7 no.4
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• pp.51-68
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• 2017

Financial technology (FinTech) is an emerging financial service sector include innovations in financial literacy and investment, retail banking, education, and crypto-currencies like bitcoin. One of the crucial branch of financial technology-third-party payment (TPP) is undergoing rapid growth, with online/mobile systems replacing offline financial systems. System quality and user attitudes are key perceptions driving third-party payment usage, the importance of these perceptions, however, may be different with countries as users' thinking varies from country to country. Thus, the purpose of this study is to elaborate how factors differ from China to Korea by drawing on the unified theory of acceptance and use of technology (UTAUT2). Additionally, this study also aims to propose a multi-attribute evaluation of the third-party online payment system based on analytic hierarchy process (AHP), fuzzy sets and technique for order performance by similarity to ideal solution (TOPSIS), to examine the relative importance of the perceptions influencing new technology adoption intention. The results showed that the price value has the most significant influence on Chinese perceptions, while the perceived credibility has the most significant effect on Korean perceptions. Sub-criteria also performs different results to Chinese and Korean third-party online payment system.

• Journal of Digital Convergence
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• v.16 no.7
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• pp.107-116
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• 2018

One of the key mission is to develop and prompte the use National Competency Standards, which is defined to be the systemization of competencies(knowledge, skills and attitudes) required to perform duties at the workplace by the nation for each industrial sector and level. This provides the basis for the design of training and detailed specifications for workplace assessment. To promote the data-driven service improvement, the commercial product Splunk was introduced, and has grown to become an extremely useful platform because it enables the users to search, collect, and organize data in a far more comprehensive, far less labor-intensive way than traditional databases. Leveraging Splunk's built-in data visualization and analytical features, HRD Korea have built custom tools to gain new insight and operational intelligence that organizations have never had before. This paper analyzes the NCS homepage. Concretely, applying Splunk in creating visualizations, dashboards and performing various functional and statistical analysis and structure without Web development skills. We presented practical use and implications through case studies.

• Trans-
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• v.10
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• pp.73-113
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• 2021

Previous studies show that VR images can influence consumers' attitudes and behaviors by evoking imagination. In this study, we introduce a reality-based closed-loop 3D image (hereafter Virtualgraph). Then we try to see whether such image would increase evocativeness in a mobile commerce environment and whether higher telepresence of the visual image of a product can increase the purchase intention of that product. In order to find the above, we developed a model comprised of constructs containing telepresence, perceived value price, perceived food quality, and vividness of visual imagery questionnaire (VVIQ). We used Virtualgraph application to conduct an experiment, and then conducted an interview as well as a survey. As results of the experiment, survey and interview, we found the followings. First, users evoke imagination better with Virtualgraph than with still images. Second, increased evocativeness affects purchase intention if the perceived quality of fresh food product is satif actory. Third, increased evocativeness makes users value products higher and do even much higher when the perceived quality of fresh food product is good. From the interview, we could find that the experimental group had higher purchase intentions and perceived products as more expensive ones. Also, they perceived images of products clearer and more vivid than did the control group. We also discuss the strategic implications of using Virtualgraph in mobile shopping malls.

• The Journal of the Korea Contents Association
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• v.21 no.5
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• pp.918-940
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• 2021

In this study, the concept of eXtended Reality Devices(XR) is defined, how it is applied by industry and how it will develop in the future, and based on the expanded integrated technology acceptance theory and innovation resistance, We tried to confirm through empirical analysis how the influencing variables affect. We carry out the analysis of the hypotheses using PLS Structural Equation Modeling. According to the empirical analysis results, this study confirms that innovativeness has a significant effect on UTAUT2's acceptance variables(performance expectation, effort expectation, hedonic motivation, price value) for XR devices, and these variables affect attitudes and acceptance of XR. and the pace of change of XR has a significant effect on perceived risk, and the perceived risk perceived by consumers mediates the pace of change and innovation resistance, and has a significant effect on innovation resistance. and innovation resistance to XR devices had a significant negative effect on acceptance. This study has its meaning because it found out that it deals expansively and comprehensively with personal innovation, the UTAUT2's acceptance variables, and the effects of perceived risk factors mediating the pace of change and resistance to innovation. In addition, it suggests that in order for innovative technologies such as XR to advance to the stage of market expansion, it is important to present strategies to reduce resistance to new technologies as much as the value to be provided to consumers.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.241-249
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• 2022

Artificial intelligence is applied not only to the daily life of individuals but also to all industries, and it is no wonder that the age of artificial intelligence has arrived. Therefore it is important to understand the factors that influence the acceptance of AI. This study analyzes whether "AI Effect" which recognizes that commercialized or familiar artificial intelligence is no longer artificial intelligence, affects the acceptance of artificial intelligence and proposes an acceptance plan based on the results. Two experiments were conducted. The first experiment was conducted on 105 adults in the result it was found that 32.4% (34 people) had AI Effect, AI Effect existed in 43.6% (24 people) of women and 20% (10 people) of men, that is, the proportion of AI Effect exsitence in women is about twice as high.and AI Effect exists when the level of AI knowledge is low. The second experiment was conducted 240 adults and 85 participants with AI Effect were selected. We found the group that recognized experience of AI accepted AI more actively. Understanding of AI Effect is expected to suggest companies' views in order to enhance AI capabilities and acceptance. In addition, future studies are expected on considering individual differences or related to acceptance attitudes.

• Information Systems Review
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• v.22 no.4
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• pp.75-92
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• 2020

As population and generation structures change, more and more customers tend to avoid facing relation due to the development of information technology and spread of smart phones. This phenomenon consists with efficiency and immediacy, which are the consumption patterns of modern customers who are used to information technology, so offline network-oriented distribution companies actively try to switch their sales and services to untact patterns. Recently, untact services are boosted in various fields, but beauty products are not easy to be recommended through untact services due to many options depending on skin types and conditions. There have been many studies on recommendations and development of recommendation systems in the online beauty field, but most of them are the ones that develop recommendation algorithm using survey or social data. In other words, there were not enough studies that classify segments based on user information such as skin types and product preference. Therefore, this study classifies customer segments using machine learning technique K-prototypesalgorithm based on customer information and search log data of mobile application, which is one of untact services in the beauty field, based on which, untact marketing strategy is suggested. This study expands the scope of the previous literature by classifying customer segments using the machine learning technique. This study is practically meaningful in that it classifies customer segments by reflecting new consumption trend of untact service, and based on this, it suggests a specific plan that can be used in untact services of the beauty field.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• The Journal of Information Systems
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• v.24 no.2
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• pp.23-52
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• 2015

Purpose This study aims to understand the role of digital knowledge in accepting the green technology. This study combined digital option theory with the second version of the Unified Theory of Acceptance and Use of Technology (UTAUT2). Contrary to other studies in which the UTAUT2 is used to explain IT adoption behavior, we look at the relationship between IT and the UTAUT2 from a new angle, incorporating an important aspect of IT, that is, digitized knowledge richness, as a determinant of the UTAUT2. Design/methodology/approach Grounded in the UTAUT2, a content analysis was conducted to investigate novel constructs dedicated to explaining green technology adoption. In this study, an amended version of the UTAUT2 specific to green technology is offered that better explains the green technology adoption behavior of consumers. Using the items identified by content analysis, we developed a questionnaire with 36 survey items. We measured all the items on a seven-point Likert-type scale. We randomly selected 402 survey respondents from a set of panel data. After a pilot study, we analyzed the main survey data by using PLS 2.0M3 and SPSS 20.0, and employed structural equation modeling to test the hypotheses. Findings The results suggest that the UTAUT2 was found to be extendable to technologies other than conventional IT. Social influence is more significant than conventional utilitarian and hedonic-based constructs such as those utilized in the UTAUT and UTAUT2 in explaining adoption behavior in the context of green technologies. The hypothesized connection between digitized knowledge richness and adoption intention was supported by the results of studies on the role of IT in formation of attitudes toward eco-friendly production. The results also indicate that digital knowledge can also encourage people to try green technology when they learn that their peers are already using the technology successfully.