Impact of End-User Privacy Enhancing Technologies (PETs) on Firms’ Analytics Performance

Big data analytics in digital commerce requires vast amounts of personal information from consumers, but this gives rise to major privacy concerns. To combat the threat of privacy invasion, individuals are proactively adopting privacy enhancing technologies (PETs) to protect their personal information. Consumers’ adoption of PETs may hamper firms’ big data analytics capabilities and performance but our knowledge of how PETs impact firms’ data analytics is limited. This study proposes an inductively derived framework which qualitatively shows that end-user PETs induce measurement error and/or missing values with regards to attributes, entities and relationships in firms’ customer databases, but the impacts of specific end-user PETs may vary by analytics use case. Our simulation experiments in the context of product recommendations quantitively find that consumers’ adoption characteristics (adoption rate and pattern) and PETs characteristics (protection mechanism and intensity) significantly affect the performance of recommender systems.