Crowd mining as a strategic resource for innovation seekers

Riccardo Bonazzi, Gianluigi Viscusi and Adriano Solidoro

Technovation, 2024, vol. 132, issue C

Abstract: This article explores how to help people who organize crowdsourcing events (called “seekers”) choose the best ideas from those submitted by participants (called “solvers'). To this end, we created a method using techniques like topic modeling and text analysis to sort and group ideas. Then, we tested this method on data from crowdsourcing contests in Italy in 2021. In particular, considering the literature on intermediaries, we focus on intermediation in crowdsourcing to improve the decision-making processes in those initiatives where searching activities are intermediated by digital platforms, besides other human intermediaries. This method makes it easier for seekers to handle multiple ideas, and it also helps them find better-quality ideas. Moreover, from a theoretical point of view, our method could lead to better results in crowdsourcing challenges because it groups ideas based on their content without being influenced by the organizers' pre-existing knowledge or biases. This means that seekers might discover new and unexpected topics or solutions they hadn't thought of before. From a practical standpoint, for managers organizing crowdsourcing events, this method is valuable because it not only saves time and effort but also potentially uncovers innovative and diverse ideas. Additionally, the method includes a feature that shows how much participants interact and share knowledge, thus implementing the concept of “transactivity”, which, to the best of our knowledge, hasn't been used in crowdsourcing studies before. This can help crowdsourcing organizers better understand which contests are more effective at encouraging collaboration and knowledge sharing among participants.

Keywords: Crowdsourcing; Crowd-driven innovation; Bounded rationality; Idea filtering; Topic modeling; Transactivity; Intermediation; Intermediary (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:techno:v:132:y:2024:i:c:s0166497224000191

DOI: 10.1016/j.technovation.2024.102969

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