Privacy and data: an empirical study of the influence of types of data and situational context upon privacy perceptions

Computers influence virtually every aspect of modern life. A primary area of concern is the impact of computers on personal privacy. This concern results from the computers capacity to receive, store, process, and output large quantities of information. This study examines the privacy sensitivity of 56 pieces of personal information in four situations including a mortgage loan application, an employment application, an insurance policy application, and a mailed marketing survey. The results will be of practical value to all holders and users of personal information. Understanding what type of personal information may or may not be privacy sensitive in a specific situation could provide specific guidance to privacy policy makers. To study privacy concerns, a survey containing 56 types of personal data was given to 1031 Alabama residents. The data was collected at five malls located throughout Alabama in the late summer of 1992. The respondents were presented a questionnaire describing one of the four situational uses. They were asked to score each of 56 types of data on a six-point, Likert-type scale. The points of the scale ranged from very unconcerned to very concerned. A Hotelling T$\sp2$ and Duncan's range tests found there is a statistically significant difference $(\alpha$ = 0.05) in privacy concern between different types of personal data. Individuals, in general, were found to have a low level of privacy sensitivity for 13 types of personal information and a high level of privacy sensitivity for 8 types. Further examination using factor analysis resulted in the grouping of the data items into eight factors. A statistically significant difference in privacy sensitivity for different situational uses of the data was also found using Hotelling T$\sp2$ and Duncan's range tests $(\alpha$ = 0.05). Marketing surveys have the highest privacy sensitivity mean score followed by an employment application, an insurance application, and the lowest was a mortgage loan application. A framework for the study of data privacy was developed and used as a basis for this research. The framework places privacy variables into an input-process-output model. This framework can be used for further research in the study of data privacy.