A MCDM methods based TAM for deriving influences of privacy paradox on user’s trust on social networks

Chi Yo Huang, Hsin Hung Wu, Hsueh Hsin Lu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Social network (SN) sites (SNSs) surged recently all over the world and have become new platforms for intimate communications. As the functionality of SNs was enhanced, users’ own information can be collected, stored, and manipulated much more easily. Privacy concerns have thus become the most concerned issue by both users and SN service providers. The service providers intend to maximize the profits and need to consider how users’ confidential information can be fully utilized in marketing and operations. At the same time, users usually concern over the misuse of private information by the website operations at the moment when disclosing individual details on SNSs. Apparently, a significant gap exists between the website operators’ intention to fully utilize the private information as well as the users’ privacy concerns about disclosing information on the SNSs. Such a cognition gap, or the “privacy paradox”, influences users’ trust on a specific SNS directly and further influences users’ acceptance and continuous usage of the sites. In this study, the Technology Acceptance Model (TAM) was introduced as the theoretical basis by applying users’ private disclosure behavior, disclosure risks perception, and the extent of privacy settings in the SNSs as the main variables. In addition, in the past works, researchers found that perceived usefulness, perceived ease of use and the interaction strength for modern technology services or products influence the use intention. So, these factors were also added as research variables in the analytic model. The Decision Making Trial and Evaluation Laboratory Based Network Process (DNP) was introduced construct the influence relationships between the variables. The weights being associated with the variables can be derived accordingly. By using the analytic model, the variables which can influence the privacy paradox on user’s trust of SNs can be derived. Such variables and influence relationships can be used in developing the security policies of the SNSs.

Original languageEnglish
Title of host publicationTrends in Applied Knowledge-Based Systems and Data Science - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Proceedings
EditorsMoonis Ali, Hamido Fujita, Jun Sasaki, Masaki Kurematsu, Ali Selamat
PublisherSpringer Verlag
Pages356-363
Number of pages8
ISBN (Print)9783319420066
DOIs
Publication statusPublished - 2016 Jan 1
Event29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016 - Morioka, Japan
Duration: 2016 Aug 22016 Aug 4

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9799
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016
CountryJapan
CityMorioka
Period16/8/216/8/4

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Keywords

  • Decision making trial and evaluation laboratory (DEMATEL)
  • Decision making trial and evaluation laboratory based network process (DANP)
  • Privacy paradox
  • Social networking sites (SNS)
  • TAM (technology acceptance model)

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Huang, C. Y., Wu, H. H., & Lu, H. H. (2016). A MCDM methods based TAM for deriving influences of privacy paradox on user’s trust on social networks. In M. Ali, H. Fujita, J. Sasaki, M. Kurematsu, & A. Selamat (Eds.), Trends in Applied Knowledge-Based Systems and Data Science - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Proceedings (pp. 356-363). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9799). Springer Verlag. https://doi.org/10.1007/978-3-319-42007-3_30