Best Paper Award in Creativity and Innovation Management (CIM)

Kasper Christensen, Sladjana Nørskov, Lars Frederiksen and Joachim Scholderer have won the CIM Best Paper Award 2017 for "In Search of New Product Ideas: Identifying Ideas in Online Communities by Machine Learning and Text Mining"

2018.03.07 | Merete Elmann

Kasper Christensen, Sladjana Nørskov, Lars Frederiksen and Joachim Scholderer

The paper “In Search of New Product Ideas: Identifying Ideas in Online Communities by Machine Learning and Text Mining”, by Kasper Christensen, Sladjana Nørskov, Lars Frederiksen, Joachim Scholderer (Issue 26.1, March 2017) was selected by the editorial board of CIM as best paper 2017. CIM is a classic for research on innovation. It has a publication record of more than 26 years and is listed as BFI 2.

The paper demonstrates that MGMT research has ventured into domains of machine learning and text mining.

 

Abstract
Online communities are attractive sources of ideas relevant for new product development and innovation. However, making sense of the 'big data' in these communities is a complex analytical task. A systematic way of dealing with these data is needed to exploit their potential for boosting companies' innovation performance. We propose a method for analysing online community data with a special focus on identifying ideas. We employ a research design where two human raters classified 3,000 texts extracted from an online community, according to whether the text contained an idea. Among the 3,000, 137 idea texts and 2,666 non-idea texts were identified. The human raters could not agree on the remaining 197 texts. These texts were omitted from the analysis. The remaining 2,803 texts were processed by using text mining techniques and used to train a classification model. We describe how to tune the model and which text mining steps to perform. We conclude that machine learning and text mining can be useful for detecting ideas in online communities. The method can help researchers and firms identify ideas hidden in large amounts of texts. Also, it is interesting in its own right that machine learning can be used to detect ideas.

Awards