Earticle

This study aims to investigate the usage patterns of users in Social Network Services (SNS) where is an upsurge. Specifically, the paper considers the reason why young people more and more prefer online (or mobile) SNS activities rather than offline face-to-face social relationship. Furthermore, the drivers which affect SNS usages are considered from users’ personal characteristics and social influences. User’s personal characteristics include their personalities (extraversion and introversion), narcissism, and life satisfaction. Social influences involve subjective norm, visibility, and image. Affected by personal and social factors in SNS, users intend to show positive self-presentation, which refers to a behavior to selectively expose his/her goodness to others. As one of the most influential drivers affecting SNS usage, the positive self-representation has an effect on the level of SNS usage. Thus, this paper suggests the hypothesized research model focusing on positive self-representation in the relationship among personal characteristics, social influence, and user’s behavior in SNS. Empirical data analysis with 100 questionnaires suggests that all hypotheses were adopted except for the effect of visibility among social influence factors on positive self-representation.

In order to reduce the spread of illegal digital contents, many studies have been focusing on how to stop it through laws and regulations. Only few of the studies tried to explain the illegal behaviors from individual‘s viewpoints. This paper aims to examine the intention of sharing illegal digital contents over the Internet and proposes a theoretical model based on the Theory of Planned Behavior (TPB) along with the introduction of two outcome expectations. We also argue that the individuals’ cognition of online copyright will influence individuals’ illegal contents sharing on the Internet. We have collected data from online survey and offline interview. By empirical study, the results support the theoretical model except the subjective norm which has no effect on individuals’ behavior. This is a different finding from the previous researches revealing that the subjective norm has no effect on individuals’ unethical actions. Overall, the findings provide strong confirmation that attitude, self-efficacy and outcome expectations impact on individual’s intention of sharing illegal contents. In addition, this study proposed an improved cognition of online copyright through education and standard of new media management will reduce illegal contents sharing on Internet.

This paper presents a method for accurate camera self-calibration based on SIFT Feature Detection and image quality assessment. We performed image quality assessment to select high quality images for the camera self-calibration process. We defined high quality images as those that contain little or no blur, and have maximum contrast among images captured within a short period. The image quality assessment includes blur detection and contrast assessment. Blur detection is based on the statistical analysis of energy and standard deviation of high frequency components of the images using Discrete Cosine Transform. Contrast assessment is based on contrast measurement and selection of the high contrast images among some images captured in a short period. Experimental results show little or no distortion in the perspective view of the images. Thus, the suggested method achieves camera self-calibration accuracy of approximately 93%.

This empirical study explored characteristics that affect the usefulness of online reviews, in the China e-commerce platform, and implemented multiple regressions to find factors that significantly influence on product sales, ultimately. Till now, prior studies have continuously revealed what factor affects usefulness of online review or product sales, only in respective terms. The point of our study is that we built two-level regression models, thereby being able to comprehensively analyze these two different targets. Before plunging into running regressions, we carefully collected 192,764 online review data for 200 products extracted from the Jingdong, the second biggest e-commerce platform in China. Also, we gathered “review sentimental scores” variable from each review and used that one as a core variable in our regression model, thus we were able to implement both quantitative and qualitative research. The evidences from the two-level regression models showed that the extent to which a product is experience good positively affects both usefulness of a review and product sales, again the usefulness of a review contributes to product sales in sequence. Also, the property of experience good has interaction effect on both for two-level regression models. Our main findings highlight the importance of role of online review to business performance of e-commerce firms.

Despite of increasing studies for product recommendation, the recommendation of product repurchase timing has not yet been studied actively. This study aims to propose deep neural network models usingsimple purchase history data to predict the repurchase timing of each customer and compare performances of the models from the perspective of prediction quality, including expected ROI of promotion, variability of precision and recall, and diversity of target selection for promotion. As an experiment result, a recurrent neural network (RNN) model showed higher promotion ROI and the smaller variability compared to MLP and other models. The proposed model can be used to develop a CRM system that can offer SMS or app-based promotionsto the customer at the right time. This model can also be used to increase sales for product repurchase businesses by balancing the level of ordersas well as inducing repurchases by customers.

The purpose of this study is to examine the direction of the students’ satisfaction improvement by comparison of related disciplines of undergraduate management information systems (MIS) majors. MIS is one of the academic disciplines that have become important as the demand for manpower for new industries in the 4th industrial revolution. We surveyed 3rd and 4th grade undergraduate students in the departments of management information systems, business administration (BA), computer engineering (CE) of C university. The results revealed that the students’ satisfaction was influenced by the ability of the professor, the curriculum development, and the curriculum operation factors. Especially, it was found that the curriculum operating factors among the three departments had greater effect on the students’ satisfaction. If the education environment is improved considering curriculum operating factors and the administrative and service factors and that are important in the CE departments, it will be helpful for the students’ satisfaction improvement of MIS.

In this study, we was compared the reliability performance of the software reliability model, which applied the Goel-Okumoto model developed using the exponential distribution, to the logarithmic function modifying the intensity function and the Rayleigh form. As a result, the log-type model is relatively smaller in the mean squared error compared to the Rayleigh model and the Goel-Okumoto model. The logarithmic model is more efficient because of the determination coefficient is relatively higher than the Goel-Okumoto model. The estimated determination coefficient of the proposed model was estimated to be more than 80% which is a useful model in the field of software reliability. Reliability has been shown to be relatively higher in the log-type model than the Rayleigh model and the Goel-Okumoto model as the mission time has elapsed. Through this study, software designer and users can identify the software failure characteristics using mean square error, decision coefficient. The confidence interval can be used as a basic guideline when applying the intensity function that reflects the characteristics of the lifetime distribution.

This study examines the reason why consumers try to use the online shopping cart more, and especially analyzes the influence of personal psychological factor such as regulatory focus. Hypotheses are tested with a two-way ANOVA model using experimental data collected from 210 undergraduate students at a business school in South Korea. We found that prevention focused consumers try to use online shopping cart more than promotion focused consumers. It is possible that prevention focused consumers try to consider as many alternatives as possible to ensure safety as they focus on negative results or losses during shopping online. However, we also found out when the buying purpose is utilitarian, promotion focused consumers are not different from the promotion in terms of the usage intention of online shopping carts. Marketing managers can provide different messages customized for their consumers by leading them to use the online shopping cart in a more effective way.

The technical definition of Blockchain is commonly known ‘distributed ledger’, however, there is no legal definition for being accepted in worldwide. Therefore, unless legal definitions and concepts of Blockchain are presented, there is a possibility that various legal disputes will occur in the future in Blockchain environment. The purpose of this study is to derive legal issues related to personal information protection that can be conflicted in Blockchain environment based on domestic Privacy Act and GDPR. The outcomes of this study can prevent various legal disputes and provide solutions that may occur due to the spread of Blockchain. It also suggests the foundation for the improvement of Privacy Act. Finally, it contributes to activate of Blockchain, industry, in Korea.

The changes of internet environment have made services through web application server (WAS) popular. Accordingly, technical difficulties in identifying users who access databases through WAS were incurred. In order to solve these problems, many companies adopt an agent-based approach for identifying users by installing additional software on WAS. However, this approach must submit to some disadvantages in terms of cost, maintenance, and development process. In this paper, we devise an non-agent based approach for identifying database users in 3-tier environments.