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. Many studies have made contributions to digital technologies (Shiau and George, 2014; Shiau and Chaun 2016; Lee and Pan, 2023). Industrial Management & Data Systems (IMDS) has made significant contributions in these areas, particularly through empirical studies grounded in structured data and quantitative modeling.
However, many existing studies still rely predominantly on either primary or secondary data, with relatively few exploring the benefits of strategically combining the two. While this has yielded valuable insights, the underutilization of mixed- or multi-method research designs represents a missed opportunity to fully capture the richness of digital-industrial contexts. 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 (Zachariadis et al., 2013; Bazeley, 2024).
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 (Venkatesh et al., 2013; Shiau and Chaun 2016; Lee and Pan, 2023). 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 such as the MMIQF (Fàbregues et al., 2024) 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 (Guetterman et al., 2024).
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.
List of Topic Areas
The guest editors welcome submissions that apply or advance mixed-/multi-method approaches to address complex issues in Information Systems and related management domains-such as marketing, MIS, operations, strategy, innovation, and organizational behavior. We particularly encourage papers that integrate diverse methods and demonstrate strong empirical relevance. For example, authors may combine structural equation modeling with qualitative interviews, pair experiments with content analysis, or integrate case studies with text mining to provide richer, multi-layered insights.
Topics of interest include, but are not limited to, the following areas:
- 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.
Journal Information: Scopus Journal Q1, H-Index 132
Submission Information
Submissions are made using ScholarOne Manuscripts. Registration and access are available here:
Author guidelines must be strictly followed. Please see:
Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to “Please select the issue you are submitting to”.
Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.
Key Dates
Opening date for manuscript submissions: 2 April 2026
Closing date for manuscript submissions: 30 September 2026
For more details refer here
