How Topic Modeling Can Spur Innovation Management
kHUB post date: September 2025
Originally published: June 7, 2025 (PDMA JPIM • Vol. 42, Issue 5 • September 2025)
Read time: 60 minutes
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The avalanche of available unstructured text data makes it ever more challenging for innovation practitioners (and academics) to extract meaningful insights from such data. Topic modeling can support these efforts and help spur innovation. The current study reviewed 1099 innovation management articles to identify and compare the most frequently used probabilistic topic modeling approaches for innovation. In an effort to contextualize the suitability of these approaches, we develop a framework to organize existing topic modeling applications along the different innovation stages (i.e., idea generation, development, and commercialization) and innovation research in general. By zooming in on the three innovation stages, the authors showcase how topic modeling can spur innovation within each stage and highlight the future potential of the specific approaches. To further assist in capturing the various dynamics in complex unstructured text datasets, we illustratively apply a tailored topic modeling configuration to 1444 Journal of Product Innovation Management articles (1984–2023) to identify emerging, stable, and mature topics, as well as looking at their respective impact. This demonstration could serve as a starting point or blueprint for innovation practitioners and researchers seeking to combine the advantages of several topic modeling approaches. We conclude by offering a future outlook, including a forward-looking research agenda. Taken together, our study offers guidance to and equips innovation practitioners and academics to design distinctive topic modeling procedures to best serve their intended purposes. If deployed appropriately, topic modeling helps users extract a wealth of unique, unprecedented insights from a continuously expanding source of data.
Practitioner Points:
- Successfully integrating topic modeling in innovation management can help to optimize innovation processes and inform decision making by extracting unique actionable insights from unstructured text data.
- Our structured overview of the most frequently used probabilistic topic modeling approaches in innovation management provides an accessible starting point for developing a deeper understanding of key approaches, including main strengths and limitations.
- The contextualized framework of topic modeling applications in innovation management showcases how the identified approaches can be used to offer novel insights within each of the fundamental stages of the innovation process, which is further complemented by outlining the future potential of the specific topic modeling approaches.
- Our tailored illustrative topic modeling application could be used as a blueprint for configuring specific topic modeling procedures that combine the advantages of different approaches to best serve intended purposes.