The Empirical Research on the Willingness of Information Disclosure for Mobile Social Platform Users---Taking Wechat as an Example
DOI: 10.23977/crypis.2019.11001 | Downloads: 13 | Views: 1741
Wang Peng 1
1 School of Economics and Management, Dalian University, No.10, Xuefu Avenue, Economic & Technical Development Zone, Dalian, Liaoning, China
Corresponding AuthorWang Peng
Based on the theory of privacy calculus and technology acceptance model (TAM), the study constructed the theoretical model on the impact factors of the information disclosure willingness of mobile social platform users. The study did the empirical study using Wechat, and chose activate Wechat users to do the research. The results showed that: perceived usefulness, perceived ease of use are positively related to the mobile social platform user information disclosure willingness , while privacy concerns is negative related to it; compared to the perceived ease of use and privacy concerns, perceived usefulness of information disclosure for users of mobile social platform is more significant; the moderating effect of privacy concerns for perceived usefulness, perceived usefulness and information disclosure willingness is not significant.
KEYWORDSMobile social platform, Information disclosure, Privacy calculus, Tam, Sem, Wechat
CITE THIS PAPER
Peng Wang, The Empirical Research on the Willingness of Information Disclosure for Mobile Social Platform Users---Taking Wechat as an Example, Crypto and Information Security (2019) Vol. 1: 1-5. DOI: http://dx.doi.org/10.23977/crypis.2019.11001.
 Wenshun Lv. (2017) Adaptive tracking control for a class of uncertain nonlinear systems with infinite number of actuator failures using neural networks. Advances in Difference Equations, 5, 96-108.
 Lei Deng. (2017) A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction. BMC Bioinformatics, 6, 356-374.
 Novanto Yudistira. (2018) Gated spatio and temporal convolutional neural network for activity recognition: towards gated multimodal deep learning. EURASIP Journal on Image and Video Processing, 1, 119-136.