The Impact Of Ego Depletion And Platform Popularity On Users’ Privacy Disclosure

Personalized recommendation platform supported by big data can reduce information overload and provide value-added services,while frequent privacy leakage events also arouse the vigilance and concern of users.With the deepening of the research,the phenomenon of "privacy paradox",which contradicts the user’s privacy concern and the actual privacy behavior,has been widely concerned.Further,recently,scholars have found that users have different self-depletion states when faced with privacy issues.However,currently,researches on self-depletion and privacy decision-making are relatively rare,so how self-depletion affects privacy cognition and actual privacy behaviors still need to be explored.Secondly,platform popularity is an indispensable external situational cue for individuals to make decisions,and the combined role of individual cognitive state and situational characteristics in behavioral decisions cannot be ignored.Under such a demand,this paper on the basis of the principle of protection motivation theory and the loss of self,from personal attributes-self loss and situational characteristics-platform the double perspective of popularity,combined with experiments,and event related potential method,to explore how ego depletion and platform popularity affect users’ privacy disclosure behavior and their cognitive neural mechanisms,In order to explain the privacy paradox more scientifically and carefully,the privacy cognition and behavior of users of personalized recommendation platform are deeply analyzed and discussed from the two levels of behavior results and EEG component data.The third chapter of this paper combines behavioral experiment and event-related potential method to analyze the data of 30 valid experiments.The results show that(1)Ego depletion affects users’ motivation to protect privacy,and then affects users’ privacy disclosure behavior.Low consumption state is an important personal reason for users to disclose their privacy despite their concern for privacy,which is confirmed by the EEG components N1 and P2,and N1 and P2 are greater at this time.(2)Platform popularity also has an impact on privacy protection motivation and subsequent disclosure behavior.High platform popularity can effectively reduce users’ privacy protection motivation and improve privacy disclosure behavior,which is an important situational reason for the inconsistency between privacy level and actual behavior,as shown by N1 being larger and P2 being smaller at this time.(3)Ego depletion and platform popularity have significant interaction effects on user privacy disclosure rate,reaction time and P2 component;(4)The three EEG components of N1,P2 and LPP respectively expressed the three stages of attention resource allocation,decision-making speed and perceived risk,and privacy protection motivation in the cognitive process of privacy disclosure.The research results opened the cognitive "black box" of users’ privacy disclosure decision-making.This study reveals the difference of information processing paths of different users with different loss in different platform popularity scenarios,makes up for the limitation of vague specific calculation paths of privacy computing theory,and puts forward suggestions for better privacy protection of users and better development of enterprises respectively.