In Search of Disruptive Ideas - Outlier Detection Techniques in Crowdsourcing Innovation Platforms

Abstract

The key challenge for data science in open innovation web systems is to find best ideas among thousands of community submissions. To date, this has been done with metrics reflecting enterprise needs or community preferences. This article proposes to look in a different direction: inspired by theoretical studies on disruptive innovation, we frame the problem of valuable ideas as those rarely taken up by masses or organisations yet having potential to change industries. Our aim is to find technological means for automatic detection of such innovations to aid decision making. Following past findings from business sciences on nature of disruptive innovations, the article presents a comparative study of multiple outlier detection algorithms applied to two real-world datasets containing textual descriptions of ideas for different industries. Obtained results demonstrate capability of outlier detection and show k-NN algorithm with TF-IDF and cosine distance to be the best candidate for the task.