Vera Schmitt
Many business models depend on the continuous collection of personal information to drive profits. Companies such as Google and Facebook require users to consistently supply data as a prerequisite for using their services. This data collection enables these businesses to generate revenue through targeted profiling and advertising. Thus, users are typically presented with two options agree to the privacy policies or do not make use of the services. The industry has attributed monetary values to personal data, utilizing it across various sectors from social media to advertising, training large machine learning models such as GPT-4, Mixtral, or Llama, and enhancing personalized products. However, the monetary evaluation of personal data from the user’s perspective remains a relatively unexplored area of research. To assess the monetary valuation of specific goods from users’ viewpoints, the concepts of Willingness to Pay (WTP) for a good and Willingness to Accept (WTA) compensation in exchange for the same good are employed. Within the realm of privacy, users face the challenge of an abstract concept of privacy, making it challenging to gauge the short-and long-term benefits and risks. Often, the implications of ongoing data sharing are not clear, leaving users with a vague understanding of the consequences. Thus, this dissertation provides a comprehensive overview of different approaches to assess the monetary value in terms of WTP and WTA. Moreover, different influencing factors are explored, to understand how among others Privacy Concerns, Privacy Behavior, Privacy Literacy, Personality Traits, and demographic indicators