User experience (UX) evaluation is widely used to assess interactive systems and support design decisions in information system engineering. Existing approaches describe UX as an effect emerging from user-product interaction and operationalize it through standardized measurement instruments. However, empirical observations suggest that UX results do not solely reflect product-related qualities but depend on evaluation prerequisites such as user needs and contextual conditions not explicitly considered in current approaches, indicating persistent interpretability limitations across diverse usage situations. This doctoral research addresses the limited scope of current UX evaluation by developing an extension that explicitly incorporates user- and context-related evaluation prerequisites. It investigates how these prerequisites shape the assessment of UX factors, what UX results represent under varying circumstances, and how structural characteristics of evaluation approaches influence result interpretability. Methodologically, the research follows the Design Science Research Methodology, grounding iterative scope development in three empirical study types: exploratory studies identify evaluation prerequisites from the user perspective, structural studies examine their relationship to existing UX measurement approaches, and explanatory studies analyze their impact on UX evaluation results in practice. The expected contribution is an empirically grounded extension of UX evaluation scope that explicates the conditions under which results can be meaningfully interpreted. By making evaluation prerequisites explicit, this research strengthens the methodological foundations of UX assessment and supports more reliable evaluation of interactive information systems.
Business Process Management (BPM) traditionally focuses on digitally mediated processes supported by Process-Aware Information Systems (PAIS). While effective in capturing control-flow events, current approaches provide limited visibility into processes unfolding within physical environments. Geospatial data, capturing real-world phenomena, is a promising tool to bridge this gap. However, existing BPM methods offer limited support for leveraging such spatio-temporal data. This research investigates how geospatial data can be integrated into BPM to bridge the gap between digital process traces and physical execution. To achieve this overarching goal, we define three objectives: (1) integrate geospatial data into PAIS to capture events and the physical execution of processes, (2) represent geospatial information in event logs while preserving spatial and temporal characteristics, and (3) embed geospatially enriched event data into process mining techniques to enable spatially-aware discovery, conformance checking, and analysis. Following an algorithm engineering methodology, this research develops artifacts to establish structured design knowledge and define a solution space for geospatially enabled BPM of real-world processes.
Modern production systems demand timely diagnostic and prognostic insights, yet the complexity of existing process intelligence (PI) tools creates a high technical barrier even for domain experts. This paper presents FIDES, a conversational neuro-symbolic tool designed to enable access to these analysis engines via natural language. Unlike approaches uniquely based on Large Language Models (LLMs) that often hallucinate operational results, FIDES implements a sound routing architecture: it uses LLMs strictly for understanding and translating the user’s intent into machine-readable encodings, while delegating the orchestration to a domain-independent automated planner. The planner autonomously decomposes complex queries and routes them to the appropriate PI engines, ensuring rigorous results. We demonstrate the tool’s maturity and usability through a lab-scale manufacturing case study, highlighting how its web-based interface enables non-technical users to perform faithful multi-perspective analysis of production processes.
