Research on the Tolerance of Privacy Leakage Among Consumers in Offline Retail Shopping Scenarios

With the continuous combination of artificial intelligence technology and the field of security, intelligent security is gradually popularized in offline retail scenes. While helping merchants to obtain and analyze consumer data and bringing certain consumption convenience to consumers, it also brings practical problems of privacy leakage. The CCTV 315 gala revealed the use of AI technology to obtain users' privacy information, helping consumers truly realize that they still need to protect their privacy information in the offline retail scene. By investigating the tolerance of privacy leakage in consumers' offline retail shopping scenarios, this paper hopes to explore how to better build a shopping environment between consumers and offline retail stores. After integrating the sensitivity of consumers' information type, the sensitivity of information receiving, information use sensitivity and related privacy theory, this paper developed and designed a third-order seventh scale. We collected data through questionnaire survey method, a total of 237 questionnaires were collected, and used to analyze the data and reliability test with SPSS software. The analysis proves that most consumers do not have a high tolerance for privacy leakage. Although there are differences between personalities, there is a centralized trend. Finally, this paper further reflects on how to build a better shopping environment and consumer experience of offline stores.