Purpose This study investigates whether the personalization–privacy paradox can be resolved under conditions not previously explored: algorithm transparency (AGT) and algorithm literacy (AGL). Design/methodology/approach An online experiment was conducted, and a moderated-moderated mediation model was assessed. Findings AGL reduced privacy concerns for highly personalized advertising, with literate consumers perceiving lower risks and showing higher click intention when algorithms were transparent. AGT proved more effective than literacy in resolving the personalization-privacy paradox. Research limitations/implications This study extends privacy paradox research by identifying how algorithm-related variables (transparency and literacy) moderate the relationship between personalization level and privacy concerns through risk perception. The findings suggest new research directions examining how varying personalization levels interact with algorithmic factors influencing consumer behavior. Practical implications Online platforms need to effectively communicate their algorithmic processes by explaining both technical operations and potential consequences of data usage. Rather than imposing penalties to reduce personalization levels, policymakers should promote AGT and enhance consumer education to help users make informed decisions about personalized advertising. Originality/value This study highlights the importance of exploring the black box of algorithms. For academia, this study expands previous findings by presenting algorithm-related conditions that serve as potential boundary conditions to resolve the personalization-privacy paradox. For the advertising industry, this study offers practitioners and policymakers insights into algorithm operation strategies rather than regulations.
Ham et al. (Sat,) studied this question.