Biosignal-adaptive platforms

Abstract Personalization is a well-established concept that leverages a wide range of user data to tailor digital platforms to target audiences. Advances in sensor technologies now allow continuous recording of human activities, such as eye gaze, heartbeat, or facial muscle movements. The resulting biosignals can be processed and interpreted in real time using, for example, artificial intelligence methods, enabling closed-loop adaptation and deeper individualized personalization. We conceptualize such systems as biosignal-adaptive platforms (BAPs). Despite their potential, research on BAPs in electronic markets remains limited. This paper addresses this gap through three key contributions: (i) we develop a morphological box that captures the technical and functional complexity of BAPs and illustrates potential solution spaces; (ii) we conduct a systematic literature review and map existing studies onto this framework, highlighting configurations currently examined in e-commerce, auctions, and streaming services; and (iii) we identify technical, methodological, ethical, and societal challenges, providing guidance for responsible, human-centered design. Together, these contributions provide an understanding of BAP’s capabilities and a foundation for future research and practice in electronic markets.