Dong Woo Kim ¹, Tae Gu Kang ², Guozhong Li ³, Seong Taek Park ⁴

Affiliations
1 The Korea Association of Software Manpower, KR
2 Department of Management Information Systems, Chungbuk National University, Heungdeok-gu, Cheongju-si, Chungcheongbuk-do, KR
3 Department of Management Information Science and Information System, Kunming University of Science of Technology, Yunnan, Kunming, Wuhua, CN
4 Department of Management Information Systems, Chungbuk National University, Heungdeok-gu, Cheongju-si, Chungcheongbuk-do, KR

Abstract

As recognition and value on big data has become significant, companies, public institutions, intelligence agencies have started to have interest on big data. Of course, there were analytic techniques based on data in past as well, but data was limited and analysis also limited. However nowadays, interest on structured data due to social media and unstructured data has started to increase that interest on big data analysis is rising. This paper systematically analyzes behavior of internet shopping mall users through big data analysis and proposes a strategic operation plan using this analysis.

Keywords

Association Rules, Big Data, Growth Factors, Shopping Mall, SOHO

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Guozhong Li ¹, Hong Jin ², Seong-Taek Park ³

Affiliations
1 Department of Management Science and Information System, Kunming University of Science and Technology, No. 68 Wenchang Road, 121 Street, Kunming, Yunnan Province, 650093, CN
2 School of Business, Jiangxi Normal University, 99 Ziyang Ave Nanchang, Jiangxi, 330022, CN
3 Department of Management Information Systems, 1 Chungdae-ro, Seowon-gu, Cheongju Chungbuk, 28644,South Korea

Abstract

Objectives: This study aims to shed light on the factors that influence mobile social networking applications (Wechat) users' intention and behaviors to share knowledge from the perspective of both media and psychological features. Methods/Statistical analysis: This research conducted questionnaire survey via online survey company (Wenjuanxing) and 197 samples were collected. Based on the data collected, research model and hypotheses were tested using smart pls 2.0. Internal consistency, convergent validity and discriminant validity were examined that were required in Partial Least Squares (PLS) analysis. Findings: The analytical results of this research show that media features-mobility and interactivity is insignificantly related with the Wechat users' intention to share knowledge. Users' psychological featuresaltruism, sense of self-worth and knowledge self-efficacy were found significantly related with users' Knowledge Sharing Intention (KSI) and Knowledge Sharing Behaviors (KSB). This research empirically examined users' psychological features which were found to be the main factors that drive individuals to participate in the knowledge sharing activities on mobile devices. These findings are particularly significant by having a deep insight into individual’s knowledge sharing activities from the perspectives of both media and users' psychological features in the mobile internet era. The results of this research are believed to contribute to the future research on people's new knowledge acquisition patterns. Application/Improvements: The results of this study will provide great benefits to companies or educational institutions with evidences of individual of their information acquisition behaviors in today's mobile internet era.

Keywords

Interactivity, Knowledge Self-Efficacy, Knowledge Sharing Behaviors, Mobility, Sense of Self-Worth, Wechat.

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Keun-Won Kim ¹, Guozhong Li ², Seong-Taek Park ³, Mi-Hyun Ko ⁴

Affiliations
1 Department of Management, Sogang University, KR
2 Department of Management Science and Information System, Kunming University of Science and Technology, CN
3 Department of Management Information Systems, Chungbuk National University, KR
4 Department of Policy Research, Korea Institute of Science and Technology Information, KR

Abstract

Background/Objectives: This study was conducted to solve the problem by predicting vaccine demand in advance through analysis of progress of birth of newborn babies in our country. Methods/Statistical Analysis: The deducted problem was defined and information, data, and use analysis method and planning procedures for creating alternatives for this issue were conducted. Afterwards, R which is an open source analysis tool was used to analysis and for visualization. In this analysis, a time series model (ARIMA model, Box-Jenkins methodology) was used to predict demand and perform the research to predict the number of births in Republic of Korea. Findings: The tuberculosis vaccines in Korea are currently being entirely of imported ones. However, the import volume often lacks meeting the demand. In this paper, research was performed to predict the demand of tuberculosis vaccines to secure vaccine stock. As result of analysis, the number of births next year was predicted to be 445,558 (in 2016). Also, analyzed results showed that approximately 388,251 to 502,864 babies will be born in reliability level of 85% and that approximately 357,915 to 533,200 babies will be born in reliability level of 95%. Vaccine should be prepared standard to the minimum value within error range because vaccines have expiration dates. Also, if more births occur than the predicted result, the issue can be coped in prior plans of preparing BCG seal-type vaccines by comparing with monthly predicted number of births. Application/Improvements: The results of this study will be applied to the ways to politically solve problems such as supply and demand of BCG vaccine for the expected newborns.

Keywords

ARIMA, Big Data, BCG Vaccine, Demand Prediction, Forecasts.

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Bit-Na Yun ¹, Dong-Hyun Lim ¹, Guozhong Li ², Seong-Taek Park ³

Affiliations
1 Departement of Business Administration, Korea National University of Transportation, Korea
2 Department of Management Science and Information System, Kunming University of Science and Technology, CN
3 Department of Management Information Systems, Chungbuk National University, Korea

Abstract

Background/Objectives: The present study performed a positive analysis on the factors affecting entrepreneurial intention with a focus on the moderating effect of self-efficacy by pre-entrepreneurs including university students and nascent entrepreneurs. Methods/Statistical Analysis: The present used Smart PLS 2.0 to verify the selected model. PLS analysis requires verification of measurement questions and constructs. Moreover the PLS method was used a suitable manner in the early stage of development of a theory that has not been verified. It is because this study has the strong nature of an exploratory study. Findings: As a study on the influence on the entrepreneurial intention of pre-entrepreneurs, the present study positively analyzed the relationship between locus of control educational factors and organizational factors, and the mediating role of self-efficacy, by using self-efficacy as a moderating variable. As a result of hypothesis verification, hypothesis 1, which claims that locus of control positively affects entrepreneurial intention, is adopted, along with hypotheses 2 and 3 which claim that educational and organizational factors positively affect entrepreneurial intention. However, the analysis of the moderating effect of self-efficacy against each factor revealed no such effect, so hypotheses was rejected. Application/Improvements: In this study, it is expected to systematically develop and apply programs for entrepreneurial candidates in terms of start-up by looking at the underlying reasons for university students to fail o the start-up.

Keywords

Enterpreneurial, Intention, Pre-Enterpreneurial, PLS, Self-Efficacy

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