Applying open science principles to our own research

Despite the growing emphasis on openness in innovation and science, we have hardly changed our own research practices as innovation management researchers. This is particularly evident in qualitative research on organization and management questions. The organization of knowledge production in this domain remains largely unchanged, with individual researchers or small teams performing all steps from data collection to publication. Could we integrate more open science principles into these processes? For instance, could we increase data sharing and reuse of existing datasets? Could we work more collaboratively?

At present, we do not fully capitalize on the significant time and effort invested in qualitative research. Qualitative data collection is typically resource-intensive, involving many interviews and observations. It can easily take one or two years to complete. As a result, qualitative studies are typically limited to one or few in-depth cases, and result in one or a few journal publications that prioritize novel insights. This leaves their potential underutilized: qualitative data is versatile and could potentially be used for alternative research questions. Open science advocates have suggested data sharing to enable reuse, yet privacy and confidentiality concerns limit the sharing of qualitative data. Anonymization might be a solution in some situations, but can also strip data of essential contextual details, thereby reducing its interpretive value. Another approach, qualitative meta-synthesis, enables the synthesis of findings from published studies but is constrained by the depth and focus of prior analyses. Thus, many studies continue to focus on novel insights from one or a few cases, limiting comparative analysis and insights into boundary conditions and generalizability. These challenges highlight the need for innovative methodological solutions.

One alternative approach is to collaborate across a larger set of researchers to reanalyze existing case studies. At the KIN department of Vrije Universiteit Amsterdam, we are experimenting with distributed re-analysis. This approach convenes multiple researchers who have conducted case studies on a particular phenomenon and engages them in reanalyzing their data according to a shared research question and shared analytical framework, while allowing them to retain control over their data. This approach has been applied from time to time by two or three researchers accidentally discovering commonalities in their studies, who then decide to combine efforts in a collaborative publication. We believe this approach can be further systematized and expanded. The novelty of this approach lies in its ability to preserve the depth of qualitative case studies while generating broader comparative insights. Unlike traditional methods that require full data sharing, distributed reanalysis enables effective collaboration while circumventing constraints related to privacy, confidentiality, and data decontextualization and leverages the tacit understandings of the original researchers.

As presented during the 2024 Open Innovation in Science conference in London, we are currently applying this method in an initial study on interorganizational collaboration. While implementing this approach presents challenges—such as identifying appropriate cases, ensuring consistency in reanalysis according to a common framework, and coordinating iterative analysis within a larger research team—our experiences so far suggest that the effort is worthwhile. This approach enhances the analytical potential of previously collected qualitative data, enabling new interpretations beyond the original scope of individual studies. Engaging multiple researchers strengthens theoretical insights and fosters richer, more nuanced interpretations. This does require many interactions with participating researchers, though, more than initially envisioned.

This approach aligns with calls for greater transparency and data reuse while addressing ethical and epistemological constraints associated with open qualitative datasets. However, questions remain regarding its scalability, applicability across different research paradigms, and the potential for deeper researcher involvement in conceptualization. Future studies may explore ways to refine this method further, ensuring its adaptability across various qualitative traditions. As an evolving experiment in open science, distributed reanalysis offers a pathway toward more rigorous, inclusive, and context-sensitive qualitative research.

This is just one example of how open science principles can be applied to management research, particularly qualitative research. Complementary initiatives include the collaborative conceptualization of a research fields, as exemplified by the foundational publication on open innovation in science (Beck et al., 2022). Yet other researchers have engaged more extensively with practitioners in the knowledge production process (Sharma & Bansal, 2020). Even the publication process itself could be reimagined—why continue to rely primarily on 20-page journal articles that become fixed once published, rather than exploring more generative, dynamic, and collaborative publication models? As researchers, we should become more experimental in applying open science principles to our own work.