< Previous   List   Next >  
Personal Information Overload and User Resistance in the Big Data Age
Full-text Download
Hwansoo Lee (Department of Management Science, KAIST)
Dongwon Lim (Department of Management Science, KAIST)
Hangjung Zo (Department of Management Science, KAIST)
Vol. 19, No. 1, Page: 125 ~ 139
Big data, Personal Information Overload, Information Privacy Concerns
Big data refers to the data that cannot be processes with conventional contemporary data technologies. As smart devices and social network services produces vast amount of data, big data attracts much attention from researchers. There are strong demands from governments and industries for big data as it can create new values by drawing business insights from data. Since various new technologies to process big data are introduced, academic communities also show much interest to the big data domain.
A notable advance related to the big data technology has been in various fields. Big data technology makes it possible to access, collect, and save individual’s personal data. These technologies enable the analysis of huge amounts of data with lower cost and less time, which is impossible to achieve with traditional methods. It even detects personal information that people do not want to open. Therefore, people using information technology such as the Internet or online services have some level of privacy concerns, and such feelings can hinder continued use of information systems. For example, SNS offers various benefits, but users are sometimes highly exposed to privacy intrusions because they write too much personal information on it. Even though users post their personal information on the Internet by themselves, the data sometimes is not under control of the users. Once the private data is posted on the Internet, it can be transferred to anywhere by a few clicks, and can be abused to create fake identity. In this way, privacy intrusion happens.
This study aims to investigate how perceived personal information overload in SNS affects user’s risk perception and information privacy concerns. Also, it examines the relationship between the concerns and user resistance behavior. A survey approach and structural equation modeling method are employed for data collection and analysis. This study contributes meaningful insights for academic researchers and policy makers who are planning to develop guidelines for privacy protection. The study shows that information overload on the social network services can bring the significant increase of users’ perceived level of privacy risks. In turn, the perceived privacy risks leads to the increased level of privacy concerns. If privacy concerns increase, it can affect users to form a negative or resistant attitude toward system use. The resistance attitude may lead users to discontinue the use of social network services. Furthermore, information overload is mediated by perceived risks to affect privacy concerns rather than has direct influence on perceived risk. It implies that resistance to the system use can be diminished by reducing perceived risks of users. Given that users’resistant behavior become salient when they have high privacy concerns, the measures to alleviate users’ privacy concerns should be conceived.
This study makes academic contribution of integrating traditional information overload theory and user resistance theory to investigate perceived privacy concerns in current IS contexts. There is little big data research which examined the technology with empirical and behavioral approach, as the research topic has just emerged. It also makes practical contributions. Information overload connects to the increased level of perceived privacy risks, and discontinued use of the information system. To keep users from departing the system, organizations should develop a system in which private data is controlled and managed with ease. This study suggests that actions to lower the level of perceived risks and privacy concerns should be taken for information systems continuance.
Show/Hide Detailed Information in Korean
빅데이터 시대의 개인정보 과잉이 사용자 저항에 미치는 영향
이환수 (카이스트 경영과학과)
임동원 (카이스트 경영과학과)
조항정 (카이스트 경영과학과)
최근 주목 받기 시작한 빅데이터 기술은 대량의 개인 정보에 대한 접근, 수집, 저장을 용이하게 할 뿐만 아니라 개인이 원하지 않는 민감한 정보까지 분석할 수 있게 한다. 이러한 기술이나 서비스를 이용하는 사람들은 어느 정도의 프라이버시 염려를 가지고 있으며, 이것은 해당 기술의 사용을 저해하는 요인으로 작용할 수 있다. 대표적 예로 소셜 네트워크 서비스의 경우, 다양한 이점이 존재하는 서비스이지만, 사용자들은 자신이 올린 수많은 개인 정보로 인해 오히려 프라이버시 침해 위험에 노출될 수 있다. 온라인 상에서 자신이 생성하거나 공개한 정보일 경우에도 이러한 정보가 의도하지 않은 방향으로 활용되거나 제 3자를 의해 악용되면서 프라이버시 문제를 일으킬 수 있다. 따라서 본 연구는 사용자들이 이러한 환경에서 인지할 수 있는 개인정보의 과잉이 프라이버시 위험과 염려에 어떠한 영향을 주는지를 살펴보고, 사용자 저항과 어떠한 관계가 있는지 분석한다. 데이터 분석을 위해 설문과 구조방정식 방법론을 활용했다. 연구 결과는 소셜 네트워크상의 개인정보 과잉 현상은 사용자들의 프라이버시 위험 인식에 영향을 주어 개인의 프라이버시 염려 수준을 증가 시키는 요인으로 작용할 수 있음을 보여준다.
Cite this article
JIIS Style
Lee, H., D. Lim, and H. Zo, "Personal Information Overload and User Resistance in the Big Data Age", Journal of Intelligence and Information Systems, Vol. 19, No. 1 (2013), 125~139.

IEEE Style
Hwansoo Lee, Dongwon Lim, and Hangjung Zo, "Personal Information Overload and User Resistance in the Big Data Age", Journal of Intelligence and Information Systems, vol. 19, no. 1, pp. 125~139, 2013.

ACM Style
Lee, H., Lim, D., and Zo, H., 2013. Personal Information Overload and User Resistance in the Big Data Age. Journal of Intelligence and Information Systems. 19, 1, 125--139.
Export Formats : BiBTeX, EndNote
Advanced Search
Date Range

author = {Lee, Hwansoo and Lim, Dongwon and Zo, Hangjung},
title = {Personal Information Overload and User Resistance in the Big Data Age},
journal = {Journal of Intelligence and Information Systems},
issue_date = {March 2013},
volume = {19},
number = {1},
month = Mar,
year = {2013},
issn = {2288-4866},
pages = {125--139},
url = { },
doi = {10.13088/jiis.2013.19.1.125},
publisher = {Korea Intelligent Information System Society},
address = {Seoul, Republic of Korea},
keywords = { Big data, Personal Information Overload and Information Privacy Concerns },
%0 Journal Article
%1 524
%A Hwansoo Lee
%A Dongwon Lim
%A Hangjung Zo
%T Personal Information Overload and User Resistance in the Big Data Age
%J Journal of Intelligence and Information Systems
%@ 2288-4866
%V 19
%N 1
%P 125-139
%D 2013
%R 10.13088/jiis.2013.19.1.125
%I Korea Intelligent Information System Society