定性比较分析
透视图(图形)
独创性
经济正义
集合(抽象数据类型)
计算机科学
功能(生物学)
价值(数学)
过程(计算)
心理学
互联网隐私
社会心理学
微观经济学
人工智能
经济
操作系统
机器学习
生物
创造力
程序设计语言
进化生物学
作者
Yongqiang Sun,Fei Zhang,Yafei Feng
标识
DOI:10.1108/ajim-06-2021-0180
摘要
Purpose This paper aimed to explain why individuals still tend to disclose their privacy information even when privacy risks are high and whether individuals disclose or withhold information following the same logic. Design/methodology/approach This study develops a configurational decision tree model (CDTM) for precisely understanding individuals' decision-making process of privacy disclosure. A survey of location-based social network service (LBSNS) users was conducted to collect data, and fuzzy-set qualitative comparative analysis (fsQCA) was adopted to validate the hypotheses. Findings This paper identified two configurations for high and low disclosure, respectively, and found that the benefits and the risks did not function independently but interdependently, and the justice would play a crucial role when both the benefits and the risks were high. Furthermore, the authors found that there were asymmetric mechanisms for high disclosure and low disclosure, and males focused more on perceived usefulness, while females concerned more about perceived enjoyment, privacy risks and perceived justice. Originality/value This paper further extends privacy calculus model (PCM) and deepens the understanding of the privacy calculus process from a configurational perspective. In addition, this study also provides guidance for future research on how to adopt the configurational approach with qualitative comparative analysis (QCA) to revise and improve relevant theories for information systems (IS) behavioral research.
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