Wir freuen uns, dass gleich vier Beiträge des EPA-Lehrstuhls auf der European, Mediterranean & Middle Eastern Conference on Information Systems (EMCIS 2026) von Clemens Brackman präsentiert wurden.

Controlling the Board: A Spatial Interaction Framework for Store-Level Market Potential in Retail IS
von Clemens Brackman

Abstract: Accurate estimation of store-level market potential is a critical capability for retail information systems supporting pricing, assortment, and replenishment decisions. Yet existing approaches typically rely on static demographic proxies that fail to capture the competitive dynamics and spatial consumer behaviour shaping actual store demand. This paper proposes a conceptual framework for estimating store-level market potential in geographically bounded local markets under limited availability of competitor sales data. Building on a localized adaptation of the Huff model, the framework integrates store attractiveness, consumer travel behaviour, and spatial demand distributions to approximate the potential customer volume a store can realistically attract relative to competing outlets. Rather than requiring observed revenue data, it infers competitive positions from observable store attributes and demographic configurations. The framework is designed for integration within retail IS, providing a spatially grounded foundation for data-driven pricing and assortment decisions at the local market level.

En Passant: How Section-Based LLM Processing Unlocks Full-Text Evidence in Systematic Literature Reviews
von Clemens Brackmann und Kathrin Nauth

Abstract: Systematic literature reviews (SLRs) are essential for synthesizing prior research, identifying trends, gaps, and future directions across various fields. However, SLRs are time-consuming and subject to human cognitive constraints that threaten their rigor, consistency, and replicability. Recent advances in generative artificial intelligence (AI) provide transformative opportunities to enhance the efficiency and scope of literature reviews by automating repetitive tasks, analysing full-text analysis, and uncovering latent patterns. We propose a step-by-step LLM-integrated methodology for SLR that covers two stages currently underserved by existing tools: abstract screening and section-based fulltext analysis. The methodology explicitly addresses three known failure modes of prior automated approaches: context-window limitations (resolved through section-based document decomposition), hallucination risks (mitigated through narrow binary prompting and mandatory validation sampling), and replicability deficits caused by opaque tool fine-tuning (resolved by using general-purpose LLMs with fully documented prompts and decision rules). Challenges related to full-text accessibility, PDF parsing, and LLM consistency are acknowledged and addressed with concrete guidance. The methodology is primarily intended for systematic and structured literature reviews where comprehensive, traceable, and replicable coverage is the goal.

Keeping AI in Check: The SCARPI Process Model for Continuous Ethical Auditing of AI-Enabled IT Systems
von Clemens Brackman

Abstract: As artificial intelligence (AI) systems become deeply embedded in organizational IT infrastructures, the need for structured mechanisms to ensure ethical compliance has become urgent. Existing governance frameworks such as COBIT and ISO 19011:2018 provide foundation but lack procedures for continuously auditing AI Systems throughout their development. Through a systematic literature review across three major databases, this paper synthesizes existing guidance to address this gap proposing the SCARPI process model comprising Define Scope, Collect and Assess, Reflect and Plan, and Implement. Grounded in established IT governance and audit structures, SCARPI enables organizations to systematically identify and mitigate bias and fairness issues in AI systems through an iterative, stakeholder-informed approach. By integrating continuous auditing with transparency artifacts such as a Public Transparency Statement, SCARPI offers a concrete, governance-aligned path toward responsible and trustworthy AI deployment. This work represents a first step toward bridging the gap between AI accountability mechanisms and existing IT governance structures.

Pricing in Tempo: A Contextualized IS Taxonomy of Pricing Strategies for Dynamic Markets
von Clemens Brackmann

Abstract: As organizations deploy increasingly autonomous IS for pricing ranging from rule-based engines to AI-driven optimization systems, the absence of a theoretically grounded classification framework impedes both the configuration of these systems and their strategic alignment. Existing pricing strategy taxonomies, developed for stable market environments, fail to account for the technological, temporal, and contextual dimensions that characterize IS-mediated pricing in dynamic markets. This research addresses that gap by developing a contextualized taxonomy of pricing strategies as a Design Science Research artifact, following the taxonomy development method of Nickerson et al. [51] as extended by Kundisch et al. [35]. Grounded in a structured literature review of 473 peerreviewed publications, the taxonomy comprises 52 characteristics across 16 dimensions, organized along four meta-dimensions: Market & Competitive Environment, Strategic Requirements, Customer & Demand, and Operationalization. Evaluation against 50 real-world case studies reveals a critical IS-strategy misalignment: while 44 of 50 cases adopt dynamic pricing strategies, only 9 leverage AI or machine learning for execution, with the majority relying on tool-based optimization. This finding demonstrates the diagnostic value of the taxonomy's Analytical Maturity dimension in surfacing the gap between strategic pricing intent and actual IS capability. The resulting artifact enables organizations to configure pricing strategies in alignment with their technological maturity and strategic context and provides IS researchers with a reusable instrument for classifying and comparing pricing system deployments across sectors.