As digital technologies increasingly transform industrial operations, integrating industrial management with information systems (IS/IT) research has become essential. Key domains such as supply chain resilience, digital innovation, and data-driven decision-making require methodological approaches that can address both technical complexity and organizational dynamics.

However, many existing studies still rely predominantly on either primary or secondary data, with relatively few exploring the benefits of strategically combining the two. Integrating primary data (e.g., surveys, interviews, experiments) with secondary data (e.g., ERP logs, platform analytics, archival datasets) enables deeper theory development by revealing both behavioral patterns and underlying mechanisms.

Mixed-methods purposefully integrate qualitative and quantitative data to provide both explanatory breadth and contextual depth—enabling researchers to explore "how" and "why" phenomena unfold. Multi-methods, which combine multiple techniques within the same paradigm, can improve robustness, triangulation, and construct validity. Both approaches are particularly valuable for studying evolving topics such as AI deployment, digital trust, platform governance, and organizational resilience.

Despite their promise, challenges persist: integration logic is often implicit, philosophical tensions may arise, and quality assessment frameworks are still emerging. Key areas remain underexplored, including how to align methodological choices with research aims, how to evaluate integration rigorously, and how to distinguish among mixed, multi, and hybrid methods in IS research.

This special issue aims to address these gaps by encouraging submissions that embrace methodological pluralism, combining primary and secondary data to produce practically relevant, empirically grounded, and theoretically robust contributions. By crossing methodological borders, we hope to foster innovative approaches that better reflect the complex realities of digital transformation in industrial systems.

Potential topics

• Mixed-/Multi-Method Designs in Digital Transformation and Process Innovation - Exploring methodological integration, trade-offs, and complementarities in examining technology-enabled changes in workflows, decision-making, and organizational processes.
• Integrated Analytical Approaches to Supply Chain Innovation and Resilience - Applying mixed-/multi-method strategies to investigate logistics transformation, disruption management, and sustainable operations in volatile environments.
• Human-AI Collaboration and Decision-Making Coevolution - Combining qualitative and quantitative methods to understand trust, cognition, and co-production dynamics in evolving human-AI systems.
• Social Media, Digital Marketing, and E-Commerce Ecosystem - Utilizing mixed-/multi-method approaches to study online engagement, influencer strategies, consumer behavior, and brand reputation in digital platforms.
• Sustainability and Green IS through Methodological Pluralism - Investigating environmental performance, circular economy practices, and sustainable innovation through integrated technical, organizational, and social lenses.
• Contextualized Adoption of Generative AI Technologies - Examining sector-specific adoption patterns, drivers, challenges, and impacts of GAI using comprehensive, multi-perspective research designs.
• Ethical, Educational, and Security Dimensions of AI - Addressing privacy, trust, risk, and AI literacy through mixed-/multi-method inquiries into responsible and secure use of AI in organizational and societal contexts.

Timeline

• April 2, 2026: Opening date for manuscript submissions
• September 30, 2026: Closing date for manuscript submissions