Crossing Methodological Borders: Mixed-/Multi-Methods in Contemporary Industrial and Information Systems management

Synthèse

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.

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Thèmes proposés

• 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.