Emerging Methods in Information Systems

Editors

• Hemang Subramanian, Florida International University
• Manjul Gupta, Florida International University

Description

Information & Management invites submissions to a Special Issue on "Emerging Methods in Information Systems." The digitization of markets, platforms, and organizations continues to reshape our methodological toolkit. Recent advances illustrate the stakes: marketplace experiments can suffer from interference that biases standard A/B test estimators, motivating designs such as cluster randomization and shadow-price–based estimators (Holtz, Lobel, Lobel, Liskovich, & Aral, 2025; Bright, Delarue, & Lobel, 2025). Across the IS field, scholars are also reframing causal identification using causal diagrams and structural causal models (Tafti & Shmueli, 2025), integrating multiple paradigms in multimethod research (Sarker et al., 2025), and exploring how generative AI augments qualitative methods such as grounded theory (Zhou, Yuan, Huang, & Hu, 2024). Meanwhile, the rapid growth of process mining has pushed data governance to the forefront of method design and validity (Goel, Martin, & ter Hofstede, 2024), and the rise of generative AI raises new concerns about fairness, bias, and evaluation (Wei, Kumar, & Zhang, 2025).

We welcome method-focused papers that advance how IS scholars design studies, build artifacts, and analyze data. Submissions may introduce new methods, adapt techniques from adjacent fields, or rigorously evaluate and synthesize emerging practices for IS contexts.

We also welcome contributions that extend or rethink established behavioral IS methods. Examples of established methods include surveys, experiments, secondary data analyses, and case studies, when these methods are adapted, combined, or reinterpreted to address emerging digital phenomena, platforms, and AI enabled systems.

Potential topics

• Experimental and quasi-experimental designs under interference and spillovers (e.g., cluster designs, network-aware randomization, market-level experiments).
• New validity frameworks and diagnostics for A/B testing in platforms and marketplaces (e.g., bias detection, power under interference, design trade-offs).
• Causal inference innovations leveraging directed acyclic graphs (DAGs), front-door/back-door criteria, and design–analysis integration for IS data.
• Multimethod and mixed-paradigm research architectures, integration logics, and evaluation criteria tailored to IS problems.
• Advances in qualitative and computational methods (e.g., LLM-assisted coding/theory building, protocol design, auditability, and reliability).
• Process mining methods with robust data governance, quality assessment, and organizational controls.
• Evaluation, fairness, and assurance methods for generative AI in IS (e.g., bias audits, safety evaluations, human-in-the-loop designs).
• Behavioral IS method innovations, including survey design, experimental protocols, secondary data strategies, and case-based approaches, when explicitly adapted to AI-mediated contexts, or data-intensive IS settings.