As the online space becomes more active, interest in protecting personal information is increasing. From this point of view, it is important to prevent personal information from being leaked in advance. As a precaution, it is suggested that users change their password periodically to protect their personal information effectively. Currently, various online services provide a request message that prompts users to periodically change their password. These messages are expressed as positive-centric or negative-centric. This message can be seen as a powerful way to trigger users' behavior. In this context, this study suggests that message framing type can be applied to the password change request message, and to investigate the difference between the positive-centric message and the negative-centric message. In addition, this study concluded that the effect of message type may be different depending on the degree of psychological ownership of the individual on the online service. As a result, users with high psychological ownership in online service were more effective when positive-centric message was presented than negative-centric message. On the other hand, users with low psychological ownership in online service were more effective when negative-centric message was presented than positive-centric message.

With the development of information technology, social network services (SNS) such as Facebook and Twitter became popular and many users disclose their personal and sensitive information like private story, photographs and location information through posting and sharing. Despite the privacy concerns in SNSs, individuals continue to disclose their identity online. This phenomenon is called 'privacy paradox'. The purpose of this study is to examine the role of collective efficacy on self-disclosure in SNS context and to explain privacy paradox phenomenon. Drawing upon the communication privacy management theory, research model was developed and empirically tested with cross-sectional data from 306 individuals. Results revealed that collective efficacy has a direct positive effect on self-disclosure while privacy risk is negatively related to self-disclosure. However, privacy concern is not directly related to self-disclosure. The relationship between privacy concern and self-disclosure was moderated by collective efficacy.

While there are lots of studies on examining the effects of social rapport in many research areas, however, there is a little work in examining the effect of the social rapport in social network service (SNS) contexts. Thus, this study attempts to examine the effect of social rapport in SNS settings. To address the research questions, this study has presented its hypotheses and conducted three experimental approaches by collecting 180 data from student subjects who have prior experiences on using SNSs to verify the hypotheses. This study has examined three experiments the effects of characteristics of Facebook(i.e. the number of mutual friends, the number of post likes, and the post personalities) on the social rapport and user responses. This study has conducted two-way ANOVA to verity its proposed research hypotheses. Based on three experiments, this study found that both the effects of the number of post likes and the number of post likes on the social rapport were not significant. Based upon empirical findings, this study has demonstrated how the effects of social rapports in SNSs were different from those of previous studies, and brought more attentions to the relevant literature.

The tourism industry is now changing to smart tourism, which maximizes tourists' overall tourism experience with the use of advanced mobile technologies and emphasizes the utilization of tourism information. Despite the quantitative expansion of the tourism industry, there is a lack of academic and practical discussion on tourism safety. Especially, in the context of walking tourism, tourists are more likely to be exposed to natural or social disasters and emergencies. Therefore, it is necessary to build a system that can provide walking tourists with safety information not only on dangerous factors which are anticipated to be confronted during a walking trip in advance but also on specific dangers in real time. Under the circumstances, this study seeks to identify the types of tourism safety information that can be offered by using publicly available open data, drawing on the safety information framework on the walking tourism that is presented in Choi et al. (2017)'s study. More specifically, this study focuses on the use of open data which is provided by the Korean government. Furthermore, this study verifies the types of safety information that are most urgently needed in walking travel situations. Specifically, this study aims to derive the importance and priority of each type of safety information for a walking trip by applying the analytic hierarchy process (AHP) analysis. For this, we collected 35 questionnaires from walking tour operators (practitioners) and walking tourists. The main results are as follows. First, natural disaster information is the most important factor in the top-level factor of safety information for walking tourists, followed by social disaster, life safety, and exhibition (security crisis) information. Second, information on natural disasters, environmental pollution, and weather is considered to be important at the sub-level factor. Lastly, the noteworthy result of this study is that the importance of each type of safety information varies depending on the walking tour operators (practitioners) and the walking tourists. That is, there is a recognition difference between the operator (practitioner) and the user in the importance and priority of the safety information of the walking trip. Therefore, it is necessary to develop policies and services reflecting the opinions of potential users when providing safety information so that the most importantly recognized information can be provided first.

The purpose of this case study is to analyze the B2C security information service model using the robo-advisor, to develop various service models and to urge new companies to enter. Overseas robo-advisor service market is growing rapidly with the launch of various B2C service models beyond B2B. On the other hand, as the domestic market is dominated by B2B services and serviced just index portfolio which is nascent, it lacks products which are used for active asset management. Recently as the government announced the approval of online investment advisory service, the B2C market of domestic asset management has entered a growth phase, centered on generations familiar with IT. We propose to extend the concept of Robo-Advisor service in accordance with the financial market change. By that model, we will study the case of the algorithm of the investment masters' philosophy and contribute to the expansion of the B2C service market.

The finance-investment industry is currently focusing on research related to artificial intelligence and big data, moving beyond conventional theories of financial engineering. However, the case of equity optimization portfolio by using an artificial intelligence, big data, and its performance is rarely realized in practice. Thus, the purpose of this study is to propose process improvements in equity selection, information analysis, and portfolio composition, and lastly an improvement in portfolio returns, with the case of an equity optimization model based on quantitative research by an artificial intelligence. This paper is an empirical study of the portfolio based on an artificial intelligence technology of "D" asset management, which is the largest domestic active-quant-fiduciary management in accordance with the purpose of this paper. This study will apply artificial intelligence to finance, analyzing financial and demand-supply information and automating factor-selection and weight of equity through machine learning based on the artificial neural network. Also, the learning the process for the composition of portfolio optimization and its performance by applying genetic algorithms to models will be documented. This study posits a model that the asset management industry can achieve, with continuous and stable excess performance, low costs and high efficiency in the process of investment.

Many advanced products and services are emerging in the market thanks to data-based technologies such as Internet (IoT), Big Data, and AI. The construction of a system for data processing under the IoT network environment is not simple in configuration, and has a lot of restrictions due to a high cost for constructing a high performance server environment. Therefore, in this paper, we will design a development environment for large data analysis computing platform using open source with low cost and practicality. Therefore, this study intends to implement a big data processing system using Raspberry Pi, an ultra-small PC environment, and open source API. This big data processing system includes building a portable server system, building a web server for web mining, developing Python IDE classes for crawling, and developing R Libraries for NLP and visualization. Through this research, we will develop a web environment that can control real-time data collection and analysis of web media in a mobile environment and present it as a curriculum for non-IT specialists.

This study develops an Education framework for users who need public open data for workplace etiquette education in a timely manner by mobile application. It facilitates utilizing efficiently Workplace etiquette contents that scattered in various platforms such as blogs, Youtube and web-sites run by private education agencies. Furthermore, it makes Public open data for workplace etiquette through gathering 'metadata', which is a comprehensive source of workplace etiquette. Accordingly, framework changes recognition about necessity of workplace etiquette education positively and suggests method that can promote effective workplace etiquette education. If the system in the study can provide public open data of workplace etiquette education, many young job applicants and workers will have a proper perception on it and sound workplace etiquette culture will be settled in the companies. Public data has been rising as a vital national strategic asset these days. Hopefully the public data will pave a way to discover the blue ocean in the market and open up a new type of businesses.

Recently, firms have been putting forth significant efforts to fulfill various demands and high expectations of customers. The role and importance of customer centers as a direct contact point for customer relationship management are more emphasized than previously. A customer center draws attention as a new alternative to secure corporate competitiveness as it contributes to sales increase, being in a position to satisfy customers' needs by ensuring customers' access to information. A customer center is an aggregation of various information and communication technologies. In particular, a voice recognition/analysis technology based on big data can elaborate customer services further, enhance customer satisfaction, and trigger constant interactions with customers. A customer center can be transformed to a hub of customer knowledge and the embodiment of business intelligence in the front line of business. This article is a case study on how the customer center of the K life insurance company regarding customer center operation collects and analyzes customer information and how it has established its voice recognition/analysis system based on big data to improve customer experience management. Factors affecting the successful introduction and implementation of voice recognition/analysis system to a firm, are examined.

The aim of this conceptual article is to explore the sharing economy ecosystem concept in innovation policy context with cluster, innovation system, smart specialization and business ecosystem approaches. This study conducts comparative study to understand what has been changed by sharing economy through Uber case in four cities. By analyzing vital constructs in sharing economy ecosystem, we suggest how sharing economy ecosystem works, and presenting core factors in policy framework of sharing economy ecosystem. In addition, we attempt to explain that policy maker should consider the relationship between these factors. The result of this paper shows sharing economy ecosystem has developed with their characteristics and constructs that are different with traditional industry.