Statement
The digitalization of knowledge work is reshaping how tasks are performed across various industries, with significant implications for individual workers and organizations. This transformation is driven by the adoption of digital work practices, distributed organizing models, AI capabilities, and advancements in knowledge synthesis. These shifts are creating new opportunities and challenges for knowledge workers, as traditional practices are reconfigured by emerging technologies.
Our focus is on understanding these shifts at the intersection of digital work, distributed organizing, and knowledge synthesis, identifying effective practices, and designing tools to meet the evolving demands of knowledge workers.
Our work builds on literature review methods, design science research, and empirical research. We work on projects in collaboration with researchers from institutions like HEC Montréal, Sydney University, the University of Virginia, Bamberg University, and Dortmund University. These joint efforts enable us to combine expertise across disciplines and leverage synergies between different research streams, allowing us to tackle both practical applications and foundational questions. This integrated approach helps us address key challenges in digital knowledge work while driving advancements that benefit both academic research and industry practices.
1. Organizing distributed work
This research stream investigates how traditional organizational hierarchiesâcharacterized by strict control and large, monolithic structuresâare evolving alongside new, more flexible forms of work organization. These include distributed (Git-based) knowledge work, open-source communities, online labor markets, and open strategy initiatives such as open employee handbooks in all-remote organizations. These models are widely used in large tech companies, knowledge-intensive industries, and academia. The primary goal is to understand how distributed work practices can be organized effectively and how to manage these work arrangements. This includes informing stakeholders about best practices for integrating these models with existing organizational frameworks.
Exemplary research questions
- Processes of knowledge-intensive crowd work How can online marketplaces for knowledge-intensive service work be distinguished? How can the micro-and macro-level processes be organized?
- Effective sourcing of knowledge work in online labor markets What explains the successful sourcing of knowledge work from online labor markets at the level of individual workers and projects?
- Boundary-spanning collaboration in commercial open source and open organizations How do firms coordinate collaboration across organizational boundaries in commercial open-source ecosystems and open handbookâbased organizations?
- HumanâAI collaboration in distributed knowledge work How can autonomous agents be integrated into Git-based knowledge work processes? How should work be organized to effectively combine automation and human oversight?
Selected publications: Wagner, Prester, & Paré (2021), Wagner & Prester (2021), Wagner & Prester (2019).
2. Digital work practices
This research stream explores the evolving role of individual knowledge workers, such as physicians, data scientists, developers, and innovative thinkers, whose work is characterized by agility, innovation, and emergent practices driven by experience and expertise rather than formalized procedures. It focuses on how digital technologies, particularly AI, are reconfiguring these work practices. Key areas of inquiry include the adoption of AI in professional settings, the interaction between AI and evidence-based practices, and how expert knowledge can be leveraged to adapt and improve enterprise systems. At the core, the goal of this research stream is to understand how knowledge work practices are reconfigured by digital technology and to provide actionable insights for leaders in knowledge-intensive fields, enabling them to capitalize on opportunities emerging from the digitalization of work, optimize the integration of new technologies, and enhance the effectiveness of digital work environments.
Exemplary research questions
- Adoption of AI What are the AI profiles of prospective physicians associated with a strong intention to use AIHT in their future medical practice?
- Interaction of AI with traditional evidence-based practices Do new AI capabilities reduce or reinforce evidence-based practices?
- Leveraging expertise to adapt enterprise systems How can the expertise of clinical experts be used to resolve misfits in enterprise systems?
Selected publications: Wagner et al. (2023), Morquin et al. (2023), Paré et al. (2023), Wagner et al. (2025).
3. Advancing knowledge synthesis
This research stream addresses the need for more efficient and effective knowledge synthesis in response to the vast and rapidly growing body of research. While the volume of available knowledge continues to increase, methods for synthesizing this researchâparticularly in the context of open and reproducible literature reviewsâare falling behind. Key areas needing improvement include search query construction, validation, knowledge record matching, emergent concept analyses, and enhancing transparency and validation through AI and generative AI (GenAI) tools. These challenges are crucial across many fields, particularly for professions like medicine, where synthesizing the best available evidence is essential. At the core, the goal of this research stream is to design tools that make knowledge synthesis processes more efficient, transparent, and reliable, benefiting practitioners, policymakers, researchers, and students who navigate both academic and industry perspectives.
Exemplary research questions
- AI-supported literature reviews How can AI and LLM capabilities support the individual steps of the literature review process?
- Understanding quality and impact of review papers What makes a literature review successful in terms of scientific impact?
- Design of open and reproducible research synthesis platforms How can tools make literature reviews and knowledge synthesis more efficient, transparent, and reliable?
Selected publications: Wagner (2024), Wagner et al. (2022), Wagner, Prester, Roche, et al. (2021), Wagner & Prester (2026), Wagner et al. (2026).