Information Systems Research Methodology for the 2030’es.
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Item type: Item , Cognitive Power for Management Through Reasoning Support(2026-01-06) Carlsson, ChristerIISR methodologies are roadmaps for infor-mation systems research that are adapted to cho-sen contexts and state-of-the-art theory to develop IS support that can give best possible guidance for (often consequential) managerial decisions. The paper works out guidance for efficient, effective or explicable management decisions. The general principles and the development history – from the early days of Operational Research and Management Science to digital ecosystem platforms for management – show how requirements on ISR methodology have evolved. An ISR methodology for the 2030’es will still rest on the decision analysis tradition to support efficient and effective managerial decisions in relevant problem-solving contexts. The flexibility and ideology of decision support systems will prevail to show how operating managers can boost performance with IS instruments, tools and technology. Digital ecosystem platforms that support the use of reasoning artificial intelligence will continue, expand and improve on the DSS ideology.Item type: Item , A Manifesto for Information Systems Research in the Age of AI Hype(2026-01-06) Keskin, TayfunThe rapid advancement of artificial intelligence technologies, especially generative and agentic AI, fundamentally challenges longstanding assumptions in Information Systems Research methodologies. Traditionally, ISR has built upon decision analysis and design science for creating decision support systems, assuming that only human cognitive power matters. However, as AI continues to develop novel cognitive capabilities autonomously, this assumption grows increasingly contestable. This paper proposes a forward-looking ISR methodology framework for the 2030s that integrates three emerging conceptual frameworks: digital coaching, digital fusion, and joint human-AI intelligence. By synthesizing insights from the evolution of ISR methodologies and analyzing the disruptive impact of modern AI, I develop methodological propositions that address the changing nature of human-machine collaboration in research processes. Future ISR methodologies must formally incorporate human-AI role definitions, treat explainability as a core outcome variable, integrate continuous learning mechanisms, draw from multidisciplinary theoretical grounding, and emphasize sociotechnical fitness while maintaining AI's proper role as a powerful tool under human direction.Item type: Item , Explain, Embed, Retrieve, and Reason (E2R2): A SHAP-Informed LLM Framework for Decision Support(2026-01-06) Davazdahemami, Behrooz; Zolbanin, Hamed; Delen, DursunThis paper introduces E2R2 (Explain, Embed, Retrieve, and Reason), a framework that combines SHAP-based feature attribution, case-based retrieval, and GPT-driven reasoning for explainable classification. Applied to student attrition prediction, E2R2 achieved 89.2% accuracy and 84.1 F1 on a 500-case holdout set, comparable to Decision Tree and Random Forest baselines while offering higher recall and balanced precision–recall. Validation showed 94% consistency across GPT sessions and resilience to incomplete data (accuracy = 86.6% under 10% feature dropout). Beyond predictive accuracy, E2R2 generates SHAP-grounded, peer-informed narratives that improve cognitive accessibility. Although demonstrated in higher education, the architecture is domain-agnostic and adaptable to fields such as healthcare or finance. By extending feature attributions into context-aware explanations, E2R2 exemplifies the design of next-generation decision support systems that combine analytic precision with interpretability.Item type: Item , Introduction to the Minitrack on Information Systems Research Methodology for the 2030’es.(2026-01-06) Liu, Yong; Carlsson, Christer; Lyytinen, Kalle; Mezei, Jozsef