A Recommender System for Privacy-Friendly Platform Business Models

Platform businesses leverage consumer data for value creation, yet offering high privacy levels could yield competitive advantages. Thus, platforms try to compete on privacy without jeopardizing their value creation potential. However, our understanding of consumer perception of privacy-friendliness and its importance for business models is limited, hindering platforms to develop privacy-friendly business models. Drawing on Design Science, we demonstrate a novel research-and-design approach, leading to a recommender system for analyzing and enhancing the privacy-friendliness of platform business models. We present a framework assessing the privacy-friendliness of platform business models, calibrated with an adaptive choice-based conjoint experiment eliciting consumers’ preferences in a representative U.S. sample. Users configure their (planned) platform business model through the recommender, which then computes a privacy-friendliness score based on our framework and offers improvement suggestions. This research aims to facilitate the design of privacy-friendly platform business models, addressing the growing importance of privacy in the digital landscape.