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Combining Humans and Machine Learning: A Novel Approach for Evaluating Crowdsourcing Contributions in Idea Contests
Journal
Multikonferenz Wirtschaftsinformatik (MKWI)
Type
conference paper
Date Issued
2018
Author(s)
Research Team
IWI6, Crowdsourcing, CCC
Abstract
The creative potential from innovative contributions of the crowd constitutes some critical challenges. The quantity of contributions and the resource demands to identify valuable ideas is high and remains challenging for firms that apply open innovation initiatives. To solve these problems, research on algorithmic approaches proved to be a valuable way by identifying metrics to distinguish between high and low-quality ideas. However, such filtering approaches always risk missing promising ideas by classifying good ideas as bad ones. In response, organizations have turned to the crowd to not just for generating ideas but also to evaluate them to filter high quality contributions. However, such crowd-based filtering approaches tend to perform poorly in practice as they make unrealistic demands on the crowd. We, therefore, conduct a design science research project to provide prescriptive knowledge on how to combine machine learning techniques with crowd evaluation to adaptively assign humans to ideas.
Language
English
Keywords
Crowdsourcing
Hybrid Intelligence
Idea Evaluation
Latent Drichilet Allocation
Machine Learning
HSG Classification
contribution to practical use / society
Event Title
Multikonferenz Wirtschaftsinformatik (MKWI)
Event Location
Lüneburg, Germany
Event Date
06.03.2018-09.03.2018
Division(s)
Eprints ID
253005
File(s)