• Title, Summary, Keyword: 소셜네트워크 인식

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Why do Customers Write Restaurant Reviews on Facebook?: An Examination into Five Motivations and Impacts of them on Perceptual Changes caused by Memory Reconstruction (왜 외식소비자들은 페이스북에 후기를 작성하는가?: 후기작성 동기와 그 동기가 기억재구성으로 인해 끼친 인식변화에 대한 고찰)

  • Noh, Jeonghee;Jun, Soo Hyun
    • The Journal of the Korea Contents Association
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    • v.14 no.8
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    • pp.416-430
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    • 2014
  • As the online word-of-mouth(WOM) using SNS has significant influence on consumer decision-making, the hospitality industry including the restaurant industry has actively used SNSs as one of major marketing tools. While researchers have focused on impacts of the online WOM, there is little research on motivations to provide WOM and its impacts on the WOM providers. The purpose of this study is to examine whether sharing the restaurant experience on Facebook, the representative SNSs, can change customer satisfaction and intentions to revisit and recommended and whether the type of motivations to share the restaurant experiences on Facebook affects customer satisfaction and intentions to revisit and recommend. The total of 260 college students volunteered to participate in this study. They first visited a restaurant and completed surveys twice before and after sharing their restaurant experience on Facebook. According to the study results, the levels of satisfaction, intention to revisit and intention to recommend after sharing the restaurant experience were found to be higher than before sharing the experience. This study also found that people who shared their restaurant experience for nostalgia were more likely to be satisfied with the restaurant services and have a higher level of intentions to revisit and recommend the restaurant. Theoretical and managerial implications as well as limitations and future research directions are discussed.

The Study of Factors to Affect on Users' Self-disclosure in Social Networking Services (SNS에서 사용자의 정보공개에 영향을 미치는 요인에 대한 연구)

  • Bang, Jounghae;Kang, Sora;Kim, Min Sun
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.17 no.8
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    • pp.69-76
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    • 2016
  • As the number of SNS users increases, so does their self-disclosure. This study examined the factors affecting self-disclosure based on Social Capital Theory and Regulatory Focus Theory. The (extent of self-disclosure by users/number of users disclosing themselves) in SNSs is expected to differ depending on their social capital (bonding capital vs. bridging capital) and regulatory focus (promotional vs. defensive). As a result of this study, it is found that bridging capital is positively related to self-disclosure in profile and in conversation, while bonding capital is positively related to self-disclosure only in conversation. With regard to regulatory focus, promotional orientation has a significant effect on self-disclosure in profile and in conversation, while defensive orientation is negatively related to self-disclosure in profile, but not related to self-disclosure in conversation. Promotional orientation is found to moderate the effect of bridging capital on self-disclosure.

Intelligent Brand Positioning Visualization System Based on Web Search Traffic Information : Focusing on Tablet PC (웹검색 트래픽 정보를 활용한 지능형 브랜드 포지셔닝 시스템 : 태블릿 PC 사례를 중심으로)

  • Jun, Seung-Pyo;Park, Do-Hyung
    • Journal of Intelligence and Information Systems
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    • v.19 no.3
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    • pp.93-111
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    • 2013
  • As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.

A MVC Framework for Visualizing Text Data (텍스트 데이터 시각화를 위한 MVC 프레임워크)

  • Choi, Kwang Sun;Jeong, Kyo Sung;Kim, Soo Dong
    • Journal of Intelligence and Information Systems
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    • v.20 no.2
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    • pp.39-58
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    • 2014
  • As the importance of big data and related technologies continues to grow in the industry, it has become highlighted to visualize results of processing and analyzing big data. Visualization of data delivers people effectiveness and clarity for understanding the result of analyzing. By the way, visualization has a role as the GUI (Graphical User Interface) that supports communications between people and analysis systems. Usually to make development and maintenance easier, these GUI parts should be loosely coupled from the parts of processing and analyzing data. And also to implement a loosely coupled architecture, it is necessary to adopt design patterns such as MVC (Model-View-Controller) which is designed for minimizing coupling between UI part and data processing part. On the other hand, big data can be classified as structured data and unstructured data. The visualization of structured data is relatively easy to unstructured data. For all that, as it has been spread out that the people utilize and analyze unstructured data, they usually develop the visualization system only for each project to overcome the limitation traditional visualization system for structured data. Furthermore, for text data which covers a huge part of unstructured data, visualization of data is more difficult. It results from the complexity of technology for analyzing text data as like linguistic analysis, text mining, social network analysis, and so on. And also those technologies are not standardized. This situation makes it more difficult to reuse the visualization system of a project to other projects. We assume that the reason is lack of commonality design of visualization system considering to expanse it to other system. In our research, we suggest a common information model for visualizing text data and propose a comprehensive and reusable framework, TexVizu, for visualizing text data. At first, we survey representative researches in text visualization era. And also we identify common elements for text visualization and common patterns among various cases of its. And then we review and analyze elements and patterns with three different viewpoints as structural viewpoint, interactive viewpoint, and semantic viewpoint. And then we design an integrated model of text data which represent elements for visualization. The structural viewpoint is for identifying structural element from various text documents as like title, author, body, and so on. The interactive viewpoint is for identifying the types of relations and interactions between text documents as like post, comment, reply and so on. The semantic viewpoint is for identifying semantic elements which extracted from analyzing text data linguistically and are represented as tags for classifying types of entity as like people, place or location, time, event and so on. After then we extract and choose common requirements for visualizing text data. The requirements are categorized as four types which are structure information, content information, relation information, trend information. Each type of requirements comprised with required visualization techniques, data and goal (what to know). These requirements are common and key requirement for design a framework which keep that a visualization system are loosely coupled from data processing or analyzing system. Finally we designed a common text visualization framework, TexVizu which is reusable and expansible for various visualization projects by collaborating with various Text Data Loader and Analytical Text Data Visualizer via common interfaces as like ITextDataLoader and IATDProvider. And also TexVisu is comprised with Analytical Text Data Model, Analytical Text Data Storage and Analytical Text Data Controller. In this framework, external components are the specifications of required interfaces for collaborating with this framework. As an experiment, we also adopt this framework into two text visualization systems as like a social opinion mining system and an online news analysis system.