Title: The 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, more and more 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 rather limited. This study proposes a theoretical framework to better understand how consumers’ use of PETs will affect firms’ analytics performance by way of inducing measurement error and/or missing values with regards to entities, attributes and relationships in firms’ customer databases. However, the impact of specific end-user PETs may vary by analytics use cases. We conduct a computational study to investigate and quantify the impact of consumer PET use on product recommendation performance. Our simulation experiments find that consumers’ adoption characteristics (adoption rate and pattern) and PETs characteristics (protection mechanism and intensity) significantly affect the performance of product recommendation systems.