Earticle

초록

영어

The amount of data a related to a person is so substantial that it appears that a digital version of them can be built thereon. They are usually handled as personal information, and the attempts made to understand personal information have led to bundling and unbundling of various data, yielding numerous fragmented categories of personal information. Therefore, we attempt to construct a generalizable lens for a deeper understanding of person-related data. We develop a theoretical framework that provides a fundamental method to understand these data as an entity of a digitally represented person based on literature review as well as the concepts of inforg and infosphere. The proposed framework suggests person-related data consist of three informational inforg dimensions that can preserve the archetype of a person, form, content, and interaction. Subsequently, the framework is examined and tested through several analyses in two different contexts: social media and online shopping mall. This framework demonstrates the suggested dimensions are interrelated with certain patterns, the prominent dimension can determine the data characteristics, and the dimensional composition of data types can imply the characteristics of the digitally represented person in certain contexts.

목차

ABSTRACT
Ⅰ. Introduction
Ⅱ. Literature Review: Data that Construct a Person as a Digital Entity
Ⅲ. Theoretical Bases of Framework: How Can a Person Be Represented in the Digital World?
3.1. Infosphere: Digital World We Live in
3.2. Inforg: A Digitally Represented Person
Ⅳ. Suggested Framework for Understanding Digital Data of a Person
Ⅴ. Applications of the Framework: Understanding Inforgs in Various Infospheres
5.1. Dimensional Associations of Framework
5.2. Clustering Data Types by Multidimensional Associations in Contexts
5.3. Comparing Clusters in Each Context from Inforg Perspective
Ⅵ. Discussion
6.1. Theoretical Implications
6.2. Practical Implications
6.3. Limitations and Future Work
Ⅶ. Conclusions
Acknowledgements

저자

• Jinyoung Min [ Associate Professor, Department of Industrial Security, Chung-Ang University, Korea ]
• HanByeol Stella Choi [ Assistant Professor, Department of Management Information Systems, Myongji University, Korea ]
• Chanhee Kwak [ Assistant Professor, Department of Artificial Intelligence Convergence, Kangnam University, Korea ]
• Junyeong Lee [ Associate Professor, Department of Management Information Systems, Chungbuk National University, Korea ] Corresponding Author

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