调节定向对App用户隐私披露的影响

p>Fully and effectively utilizing user data is an important part of the Internet economy. Data collection inevitably involves users&rsquo; privacy information and requires users&rsquo; authorization, which includes privacy disclosure. Although it is well-documented that a user&rsquo;s regulatory focus type is a robust predictor of privacy disclosure intention, few studies have examined the underlying decision-making mechanism. Group-level studies based on objective data are also limited. Thus, based on self-regulatory theory, simulated experimentations and titration procedures are first adopted to indicate the influences of users&rsquo; regulatory focus on the decision preferences and risk evaluation procedures involved in privacy disclosure, respectively. Second, a series of behavioral and eye-tracking experiments explore whether and how regulatory fits enhance a user&rsquo;s privacy disclosure intentions. Finally, machine learning algorithms are applied to perform a text analysis of a user&rsquo;s microblog to generalize the association patterns between regulatory focus and privacy disclosure behaviors at the group level. This project helps explain the mechanism of regulatory focus in the process of privacy authorization. In addition, the results are expected to have potential application values in modifying the designs of privacy authorization information.</p