Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/315272 
Year of Publication: 
2024
Citation: 
[Journal:] Group Decision and Negotiation [ISSN:] 1572-9907 [Volume:] 33 [Issue:] 5 [Publisher:] Springer Netherlands [Place:] Dordrecht [Year:] 2024 [Pages:] 1301-1322
Publisher: 
Springer Netherlands, Dordrecht
Abstract: 
Macro-task crowdsourcing presents a promising approach to address wicked problems like climate change by leveraging the collective efforts of a diverse crowd. Such macro-task crowdsourcing requires facilitation. However, in the facilitation process, traditionally aggregating and synthesizing text contributions from the crowd is labor-intensive, demanding expertise and time from facilitators. Recent advancements in large language models (LLMs) have demonstrated human-level performance in natural language processing. This paper proposes an abstract design for an information system, developed through four iterations of a prototype, to support the synthesis process of contributions using LLM-based natural language processing. The prototype demonstrated promising results, enhancing efficiency and effectiveness in synthesis activities for macro-task crowdsourcing facilitation. By streamlining the synthesis process, the proposed system significantly reduces the effort to synthesize content, allowing for stronger integration of synthesized content into the discussions to reach consensus, ideally leading to more meaningful outcomes.
Subjects: 
Action design research
Facilitation
Large language model
Macro-task crowdsourcing
Natural language processing
Synthesis
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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