Abstract Motivation: Addressing today’s social, environmental, and technological challenges requires not only strong strategies but also approaches that coordinate stakeholder activities across ecosystems. While many tools help multi-stakeholder partnerships analyze the past and envision desirable futures, stakeholders often struggle to translate those visions into actionable steps because their goals and values in the present are misaligned. This misalignment leads to planning paralysis or fragmented efforts. The Collective Action Planning (CAP) method: CAP is a facilitation method that integrates diverse stakeholder goals and perspectives to create complementary, actionable solutions. Grounded in systems thinking and pragmatism – particularly the concept of simplexity, which balances complex understanding with simple action – CAP connects requirements, constraints, and activities across ecosystems, emphasizing how pilot experiments interact with existing structures. Research Design: Developed through an echeloned Design Science Research (eDSR) process involving sixteen validation cycles in seminars, trainings, and projects, CAP now comprises five steps. Practitioner benefits: We illustrate CAP use through the case of building a circular ecosystem for reusable to-go cups. CAP enhances sensemaking and coordination in multi-stakeholder partnerships, helping actors move from shared vision to collective action – in short, it is a method for acting together.
Train managers face a challenging work environment, characterized by a heightened risk of passenger aggression within spatially confined environments. Further, the rarity of threatening incidents makes skill development difficult. This paper presents the design and development of a Virtual Reality (VR) training artifact aimed at equipping train managers with de-escalation skills through immersive VR simulation of threatening onboard scenarios. Developed in collaboration between SJ (Sweden's largest train operator), University West, and VR developer Tenstar, the project operationalizes Design Thinking (DT) as the micro-level method within an Action Design Research (ADR) framework. Two Minimum Viable Products (MVPs) were developed as boundary objects to surface tacit practitioner knowledge and validate scenarios before committing to costly VR development. A modular JSON-based architecture decouples scenario content from the VR implementation, enabling rapid iterations without modifying the underlying VR environment. Evaluation indicates strong perceived utility and confirms that the design process successfully captured the complexity of real onboard work practices. The paper contributes a methodological approach for developing VR training artifacts in complex, practice-embedded contexts.
B2B pricing in high-velocity wholesale environments is typically delegated to frontline sales agents, yet time pressure and information asymmetry lead to systematic over-discounting that erodes margins. While data-driven automation promises a remedy, the absence of rejected quotes from historical transaction data creates a fundamental censoring problem: the margin-maximizing price is likely unrecoverable, and black-box optimization cannot be validated against the true business objective. Static floor-and-target rules, the dominant alternative, ignore transaction-level heterogeneity. We present a hybrid intelligence Decision Support System developed with a large German finishing trades wholesaler processing over 3 million annual transactions. The artifact uses K-Nearest Neighbors as a Case-Based Reasoning mechanism to retrieve historical transactions with comparable elasticity profiles, defined by customer characteristics, product attributes, order size, and market timing, and constructs transaction-specific price corridors from the empirical discount distribution of the matched set. Glass-box retrieval enables domain experts to inspect neighbors, assess comparability, and iteratively recalibrate similarity weights, treating model output as a refinable heuristic rather than an authoritative optimum. An offline simulation on 160,000 recent transactions estimates revenue uplifts of 8 to 12
As Large Language Models (LLMs) become integral to design science research, a new software paradigm known as ‘promptware’ has emerged. In this paradigm, the prompts that drive artifact development are themselves critical, first-class software artifacts requiring systematic documentation and engineering rigor. However, traditional version control tools capture only code changes, leaving the rationale behind AI-assisted decisions invisible and irreproducible. LLM Buddy is a desktop-based research-instrumentation environment for eADR that captures prompts and LLM responses across interfaces, links them to the files they shaped, and preserves the resulting design rationale as an auditable, rollbackable research trace. It provides multimodal prompt capture, prompt-to-file association, automated rollback, and structured session records for eADR reporting. Developed and evaluated through six Elaborated Action Design Research (eADR) iterations, LLM Buddy captured 1,555 prompts during a longitudinal field deployment at the University of South Florida, supporting the identification of recurring prompt interaction patterns, including what the authors term Conversational Forking, and aiding recovery from a critical file corruption incident through its automated versioning system.
Detecting emerging failure patterns in automotive aftersales requires analyzing large volumes of heterogeneous repair data at very low initial occurrence rates. Machine learning can cluster similar failures, but domain expertise is crucial for early novelty detection in context-dependent environments. We present a Human-in-the-Loop Information System that integrates expert feedback into model-driven clustering. Using Design Science Research, we derived four design principles based on Cognitive Fit Theory and practitioner insights, instantiated in a web-based prototype. In empirical evaluation, field analysts indicated that aligning cluster information with analytical tasks reduced cognitive load and enhanced reasoning. This study contributes design knowledge for Human-in-the-Loop Information Systems, demonstrating how human expertise and model-driven approaches can collaborate effectively to support failure detection.
