Combining Collective and Artificial Intelligence: Towards a Design Theory for Decision Support in Crowdsourcing
Type
conference paper
Date Issued
2017
Author(s)
Research Team
IWI6, CCC, Crowdsourcing
Abstract
Crowdsourcing represents a powerful approach that seeks to harness the collective knowledge or creativity of a large and independent network of people for organizations. While the approach drastically facilitates the sourcing and aggregating of information, it represents a latent challenge for organizations to process and evaluate the vast amount of crowdsourced contributions – especially when they are submitted in an unstructured, textual format. In this study, we present an on-going design science research project that is concerned with the construction of a design theory for semi-automated information processing and decision support in crowdsourcing. The proposed concept leverages the power of crowdsourcing in combination with text mining and machine learning algorithms to make the evaluation of textual contributions more efficient and effective for decision-makers. Our work aims to provide the theoretical foundation for designing such systems in crowdsourcing. It is intended to contribute to decision support and business analytics research by outlining the capabilities of text mining and machine learning techniques in contexts that face large amounts of user-generated content. For practitioners, we provide a set of generalized design principles and design features for the implementation of these algorithms on crowdsourcing platforms.
Language
English
Keywords
Crowdsourcing
Design Science Research
Machine Learning
Text Mining
HSG Classification
contribution to practical use / society
Event Title
European Conference on Information Systems (ECIS)
Event Location
Guimarães, Portugal
Event Date
08.06.2017
Division(s)
Eprints ID
252288
File(s)![Thumbnail Image]()
open.access
Name
JML_660.pdf
Size
374.33 KB
Format
Adobe PDF
Checksum (MD5)
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