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.
Symbolic regression searches for mathematical expressions that best fit a given set of data points. In this work we use this regression method to describe phase transformations from austenite to product phases for different isothermal and non-isothermal cooling conditions with a single analytical equation. Besides the data processing steps, we describe the underlying mathematical algorithm and present an expression for the employed data set. The resulting equation describes the phase transformation rate and can be used to describe the phase transformation kinetics for arbitrary cooling paths. The result is validated on four additional measurements with different heat treatment and final microstructures. The application of the method to a wider range of chemical compositions is discussed.
Traditional two-dimensional risk matrices (heatmaps) are widely used to model and visualize likelihood and impact relationships, but they face fundamental methodological limitations when applied to complex infrastructures. In particular, regulatory frameworks such as NIS2 and DORA call for more context-sensitive and system-oriented risk analysis. We argue that incorporating contextual dimensions into heatmaps enhances their analytical value. As a first step towards our Hagenberg Risk Management Process for complex infrastructures and systems, this paper introduces a multidimensional (ND) polar heatmap as a formal model that explicitly integrates additional context dimensions and subsumes classical two-dimensional models as a special case.
Risk matrices (heatmaps) are widely used for information and cyber risk management and decision-making, yet they are often too coarse for today's resilience-driven organizational and system landscapes. Likelihood and impact (the two dimensions represented in a heatmap) can vary with operational conditions, third-party dependencies, and the effectiveness of technical and organizational controls. At the same time, organizations cannot afford to analyze and operationalize every identified risk with equal depth using more sophisticated methods, telemetry, and real-time decision logic. We therefore propose a traceable triage pipeline that connects broad, context-sensitive screening with selective deep-dive analysis of material risks. The Hagenberg Risk Management Process presented in this paper integrates three steps: (i) context-aware prioritization using multidimensional polar heatmaps to compare risks across multiple operational states, (ii) Bowtie analysis for triaged risks to structure causes, consequences, and barriers, and (iii) an automated transformation of Bowties into directed acyclic graphs as the structural basis for Bayesian networks. A distinctive feature is the explicit representation of barriers as activation nodes in the resulting graph, making control points visible and preparing for later intervention and what-if analyses. The approach is demonstrated on an instant-payments gateway scenario in which a faulty production change under peak load leads to cascading degradation and transaction loss; DORA serves as the reference framework for resilience requirements. The result is an end-to-end, tool-supported workflow that improves transparency, auditability, and operational readiness from prioritization to monitoring-oriented models.
