• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.10 no.6
• /
• pp.69-80
• /
• 2015

This can be promoted collaboration environment for the system and the system is very important for competitiveness, it is equipped. If so, could work in collaboration with members of the organization to promote collaboration what factors? Organizational collaboration and cooperation of many people working, or worth pursuing common goals by sharing information and processes to improve labor productivity, defined as collaboration. Factors that promote collaboration are shared visions, the organization's principles and rules that reflect the visions, on-line system developments, and communication methods. First, it embodies the vision shared by the more sympathetic members are active and voluntary participation in the activities of the organization can be achieved. Second, the members are aware of all the rules and principles of a united whole is accepted and leads to good performance. In addition, the ability to share sensitive business activities for self-development and also lead to work to make this a regular activity to create a team that can collaborate to help the environment and the atmosphere. Third, a systematic construction of the online collaboration system is made efficient and rapid task. According to Student team and A corporation we knew that Cloud services and social media, low-cost, high-efficiency services could achieve. The introduction of the latest information technology changes, the members of the organization's systems and active participation can take advantage of continuing education must be made. Fourth, the company to inform people both inside and outside of the organization to communicate actively to change the image of the company activities, the creation of corporate performance is very important to figure. Reflects the latest trend to actively use social media to communicate the effort is needed. For development of systematic collaboration promoting model steps to meet the organizational role. First, the Chief Executive Officer to make a firm and clear vision of the organization members to propagate the faith, empathy gives a sense of belonging should be able to have. Second, middle managers, CEO's vision is to systematically propagate the organizers rules and principles to establish a system would create. Third, general operatives internalize the vision of the company stating that the role of outside companies must adhere. The purpose of this study was well done in collaboration organizations promoting factors for strategic alignment model based on the golden circle and collaboration to understand and reflect the latest trends in information technology tools to take advantage of smart work and business know how student teams through case analysis will derive the success factors. This is the foundation for future empirical studies are expected to be present.

• Journal of Hospice and Palliative Care
• /
• v.21 no.1
• /
• pp.9-13
• /
• 2018

Purpose: The purpose of this study was to assess the factors influencing the rescue medication decisions for breakthrough cancer patients and evaluate treatments using the factors. Methods: Based on the results of an online survey conducted by the Korean Society of Hospice and Palliative Care from September 2014 through December 2014, we assessed the level of agreement on nine factors influencing rescue medication preference. The same factors were used to evaluate oral transmucosal fentanyl lozenge, oral oxycodone and intravenous morphine. Results: Agreed by 77 physicians, a rapid onset of action was the most important factor for their decision of rescue medication. Other important factors were easy administration, strong efficacy, predictable efficacy and less adverse effects. Participants agreed that intravenous morphine produced a rapid onset of action and strong and predictable efficacy and cited difficulty of administration and adverse effects as negative factors. Oral oxycodone was desirable in terms of easy administration and less adverse effects. However, its onset of action was slower than intravenous morphine. While many agreed to easy administration of oral transmucosal fentanyl lozenge, the level of agreement was low for strength and predictability of its efficacy, long-term durability and sleep improvement. Conclusion: Rapid onset of action is one of the important factors that influence physicians' selection of rescue medication. Physicians' assessment of rescue medication differed by medication.

• Journal of Intelligence and Information Systems
• /
• v.20 no.2
• /
• pp.93-107
• /
• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• Management & Information Systems Review
• /
• v.35 no.4
• /
• pp.99-122
• /
• 2016

The social networking services (SNSs) have become popular among smartphone users, and one of the most popular services. In order to explain users' motivations toward SNS, this study considers uses and gratification theory which can explain individuals' motivations to select certain media channels. The purposes of this study is to investigate the relationships between motivations and addiction of SNS, and between addiction of SNS and decline in the subjective wellbeing. We examine moderating effects of social tie based on the social capital theory in the relationships between SNS addiction and decline in the subjective wellbeing. The motivations of SNS are subdivided into emotional motive (entertainment and fantasy) and cognitive motive (information share burden and challenge burden) based on the use and gratifications theory. The addiction of SNS is subdivided into time tolerance, withdrawal symptoms, interruption, and barrier of living. The data used in this study were collected from 286 SNS users through surveys. The data analysis in this study was performed using AMOS 17.0, and we used SEM(Structural Equation Modeling) methods in order to test the research model. The result shows that the emotional motive(entertainment and fantasy) and cognitive motive(information share burden and challenge burden) have an effect on the addiction of SNS. Especially emotional motive such as entertainment and users' fantasy toward SNS is an important factor that can cause SNS addiction. The addiction of SNS such as time tolerance, withdrawal symptoms, interruption, and barrier of living has an effect on the decline in the subjective wellbeing. Our result show that social tie partially moderates the relationship SNS addiction and decline in the subjective wellbeing. In addition, social tie between interruption of SNS and decline in the subjective wellbeing is an important moderating factor. The results focuses on the understanding toward relationship between SNS addiction based on the online and decline in the subjective wellbeing in the real world. The findings of this study also provides theoretical as well as practical implications which reflect the major features of SNS, and moderating effects of social tie based on the social capital.

• Journal of Intelligence and Information Systems
• /
• v.21 no.3
• /
• pp.19-36
• /
• 2015

Social media is an emerging issue in content services and in current business environment. YouTube is the most representative social media service in the world. YouTube is different from other conventional content services in its open user participation and contents creation methods. To promote a content in YouTube, it is important to understand the diffusion phenomena of contents and the network structural characteristics. Most previous studies analyzed impact factors of contents diffusion from the view point of general behavioral factors. Currently some researchers use network structure factors. However, these two approaches have been used separately. However this study tries to analyze the general impact factors on the view count and content based network structures all together. In addition, when building a content based network, this study forms the network structure by analyzing user comments on 22,370 contents of YouTube not based on the individual user based network. From this study, we re-proved statistically the causal relations between view count and not only general factors but also network factors. Moreover by analyzing this integrated research model, we found that these factors affect the view count of YouTube according to the following order; Uploader Followers, Video Age, Betweenness Centrality, Comments, Closeness Centrality, Clustering Coefficient and Rating. However Degree Centrality and Eigenvector Centrality affect the view count negatively. From this research some strategic points for the utilizing of contents diffusion are as followings. First, it is needed to manage general factors such as the number of uploader followers or subscribers, the video age, the number of comments, average rating points, and etc. The impact of average rating points is not so much important as we thought before. However, it is needed to increase the number of uploader followers strategically and sustain the contents in the service as long as possible. Second, we need to pay attention to the impacts of betweenness centrality and closeness centrality among other network factors. Users seems to search the related subject or similar contents after watching a content. It is needed to shorten the distance between other popular contents in the service. Namely, this study showed that it is beneficial for increasing view counts by decreasing the number of search attempts and increasing similarity with many other contents. This is consistent with the result of the clustering coefficient impact analysis. Third, it is important to notice the negative impact of degree centrality and eigenvector centrality on the view count. If the number of connections with other contents is too much increased it means there are many similar contents and eventually it might distribute the view counts. Moreover, too high eigenvector centrality means that there are connections with popular contents around the content, and it might lose the view count because of the impact of the popular contents. It would be better to avoid connections with too powerful popular contents. From this study we analyzed the phenomenon and verified diffusion factors of Youtube contents by using an integrated model consisting of general factors and network structure factors. From the viewpoints of social contribution, this study might provide useful information to music or movie industry or other contents vendors for their effective contents services. This research provides basic schemes that can be applied strategically in online contents marketing. One of the limitations of this study is that this study formed a contents based network for the network structure analysis. It might be an indirect method to see the content network structure. We can use more various methods to establish direct content network. Further researches include more detailed researches like an analysis according to the types of contents or domains or characteristics of the contents or users, and etc.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.16 no.4
• /
• pp.135-151
• /
• 2021

This research attempts to analyze the factors influencing the entrepreneurial intention of employees in manufacturing field. In particular, key factors of entrepreneurship and personal characteristics explain a significant association with the intention to start-up. And study whether R&D support from public enterprise adjusts intention to entrepreneurial Intention. This study conducted a online survey on 292 small and medium-sized enterprise manufacturing employees in May 2020. Using linear regression model and binary logistic model. The main study results are the following: First, among the key factors(innovativeness, proactiveness, risk-taking) of entrepreneurship, proactiveness hardly influenced the opportunity competency. Second, among the factors(risk-taking propensity, locus of control, tolerance for ambiguity) of personal characteristics, locus of control hardly influenced the opportunity competency. Third, opportunity competency(opportunity recognition and opportunity evaluation) had positive influence to entrepreneurial intention. Fourth, the study investigated the mediated effect of opportunity competency. The result showed that among the factors of entrepreneurship and personal characteristics, only two factors that are proactiveness and locus of control were not mediated by opportunity competency. and opportunity evaluation was acted as a mediator between proactiveness and entrepreneurial Intention, compared with opportunity recognition. Lastly, public enterprise's R&D supporting moderated the entrepreneurial intention). Based on the result, the study showed that first, the key factors of entrepreneurship except for proactiveness and personal characteristics(risk-taking propensity, locus of control, tolerance for ambiguity) except for locus of control affect the intention to start-up, repeatedly. This results are explained that employees have not started a business yet. Second, research on start-up suggests the need to analyze factors differentiated before and after the start-ups. Based on the results, entrepreneurship and personal characteristics show that study on the effects of start-up intentions should be carried out before and after the actual start-up takes place, and can be used as effective data in policies to promoting start-ups in manufacturing field.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.47 no.1
• /
• pp.57-75
• /
• 2019

Soundscapes have the potential to help people experience the historical background and cultural traditions by the scenery of a local area and to be used as a cultural and tourism resource. This concept was first explained in detail by M. Schafer and has been developed as a new way of experiencing landscapes using various senses. This research studied the soundscape of Kyodong Island, the so-called "Island of Peace" and designed new cultural acoustic content for education and tourism. Kyodong Island is located right below the Northern Limit Line and the whole island is in the Civilian Controlled Area. The political and economic status of the island has been changed dynamically by the Korean War and the division of the country. These days, the island needs to realize the vision of the "Island of Peace" in a more creative way using local resources, including its "cold war landscape" and the natural scenery of the region. This research applied the concept of a soundscape to document the island, and to reproduce it in an artistic way. A workshop was conducted to learn concepts and techniques of soundscapes with a sound artist. Listening, recording, conducting interviews, and literature research was used to study the soundscape of the island. After that, this research reconstructed the soundscape of the island through a soundscape composition. The main theme of the composition story was the "Hope and Wish for the Harmony and Peace" to show the vision of the "Island of Peace". The initial sub-theme for the introduction part was "First Encounter with Kyodong Island" arranging the representative soundscape, which could be the first impression of the region. The second sub-theme was "War and Tension" using several soundscapes as a metaphor for the tragedy of the Korean War. The third sub-theme was "Everyday Life of Kyodong Island" which described the energy of the present day, after the wounds of the war have healed. The final sub-theme was "Harmony and Peace" using traditional music and keynote sounds of the region as a reminder of the peaceful past, before the war. The recording files were documented as two types of sound maps. One was a two-dimensional map to show the soundscapes from one point of view, and the other used the online application called "Sound Around You". The final artwork was displayed at an exhibition and uploaded on YouTube to be shared publicly. Through this project, we discovered the potential of soundscapes as a medium to preserve the history and local identity, as well as presenting a new vision. The artwork will be exhibited at historically and culturally meaningful places on the Island to utilize the underused places as local tourist attractions and educational resources.

• Journal of Korean Home Economics Education Association
• /
• v.33 no.3
• /
• pp.143-157
• /
• 2021

This study analyzed and verified the effects of a dietary education program for Korean young adults in single-person households. The dietary education program was conducted for five weeks (from November 10 to December 8, 2020) for Korean young adults in single-person households living in Seongbuk-gu, via four face-to-face education sessions and one online education session, including both theoretical lectures and practice. The effect of the dietary education program was analyzed through the Nutrition Quotient (NQ) questionnaire for adults provided by the Korean Nutrition Society, a dietary evaluation checklist questionnaire developed by the researchers, and photovoices. The average of 'Nutrition' score increased from 51.81 to 53.20, but there was no statistically significant difference. However, there was a significant change in the 'Moderation' category (p<0.05). As for the researcher-developed dietary evaluation, the average of the 'Importance' area rose from 3.77 to 3.99, but there was no statistically significant difference. The average of the 'Practice' area rose from 3.03 to 3.57, significant results were found (p<0.05). When the pre-/post-tests were compared by the sub-categories, four elements of 'Importance', i.e., balanced meals and avoided foods, and all five elements of 'Practice' were significantly improved (p<0.05). A total of 200 photovoices were analyzed according to Social Cognitive Theory. As a result of the analysis, the deterrents that help people eat healthy homemade food were 48% environmental factors, 30% behavioral factors, and 22% individual cognitive factors. The deterrents found to hinder participants from eating healthy homemade food were 72% environmental factors and 14% individual cognitive and behavioral factors. The results suggest that the dietary education program for Korean young adults in single-person households can be an effective tool that promotes self-motivation, behavioral changes, and improvements of the surrounding environment.

• Journal of Intelligence and Information Systems
• /
• v.27 no.1
• /
• pp.151-176
• /
• 2021

As the information technology industry develops, various kinds of data are being created, and it is now essential to process them and use them in the industry. Analyzing and utilizing various digital data collected online and offline is a necessary process to provide an appropriate experience for customers in the industry. In order to create new businesses, products, and services, it is essential to use customer data collected in various ways to deeply understand potential customers' needs and analyze behavior patterns to capture hidden signals of desire. However, it is true that research using data analysis and UX methodology, which should be conducted in parallel for effective service development, is being conducted separately and that there is a lack of examples of use in the industry. In thiswork, we construct a single process by applying data analysis methods and UX methodologies. This study is important in that it is highly likely to be used because it applies methodologies that are actively used in practice. We conducted a survey on the topic to identify and cluster the associations between factors to establish customer classification and target customers. The research methods are as follows. First, we first conduct a factor, regression analysis to determine the association between factors in the happiness data survey. Groups are grouped according to the survey results and identify the relationship between 34 questions of psychological stability, family life, relational satisfaction, health, economic satisfaction, work satisfaction, daily life satisfaction, and residential environment satisfaction. Second, we classify clusters based on factors affecting happiness and extract the optimal number of clusters. Based on the results, we cross-analyzed the characteristics of each cluster. Third, forservice definition, analysis was conducted by correlating with keywords related to happiness. We leverage keyword analysis of the thumb trend to derive ideas based on the interest and associations of the keyword. We also collected approximately 11,000 news articles based on the top three keywords that are highly related to happiness, then derived issues between keywords through text mining analysis in SAS, and utilized them in defining services after ideas were conceived. Fourth, based on the characteristics identified through data analysis, we selected segmentation and targetingappropriate for service discovery. To this end, the characteristics of the factors were grouped and selected into four groups, and the profile was drawn up and the main target customers were selected. Fifth, based on the characteristics of the main target customers, interviewers were selected and the In-depthinterviews were conducted to discover the causes of happiness, causes of unhappiness, and needs for services. Sixth, we derive customer behavior patterns based on segment results and detailed interviews, and specify the objectives associated with the characteristics. Seventh, a typical persona using qualitative surveys and a persona using data were produced to analyze each characteristic and pros and cons by comparing the two personas. Existing market segmentation classifies customers based on purchasing factors, and UX methodology measures users' behavior variables to establish criteria and redefine users' classification. Utilizing these segment classification methods, applying the process of producinguser classification and persona in UX methodology will be able to utilize them as more accurate customer classification schemes. The significance of this study is summarized in two ways: First, the idea of using data to create a variety of services was linked to the UX methodology used to plan IT services by applying it in the hot topic era. Second, we further enhance user classification by applying segment analysis methods that are not currently used well in UX methodologies. To provide a consistent experience in creating a single service, from large to small, it is necessary to define customers with common goals. To this end, it is necessary to derive persona and persuade various stakeholders. Under these circumstances, designing a consistent experience from beginning to end, through fast and concrete user descriptions, would be a very effective way to produce a successful service.

• Journal of Intelligence and Information Systems
• /
• v.27 no.1
• /
• pp.103-128
• /
• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.