Towards Artificial Intelligence Augmenting Facilitation: AI Affordances in Macro-Task Crowdsourcing

Henner Gimpel (), Vanessa Graf-Seyfried (), Robert Laubacher () and Oliver Meindl ()
Additional contact information
Henner Gimpel: Research Center Finance & Information Management, University of Hohenheim - Digital Management, Fraunhofer FIT - Branch Business & Information Systems Engineering
Vanessa Graf-Seyfried: Research Center Finance & Information Management, Branch Business & Information Systems Engineering of the Fraunhofer FIT
Robert Laubacher: Massachusetts Institute of Technology (MIT)
Oliver Meindl: Research Center Finance & Information Management, University of Hohenheim - Digital Management, Fraunhofer FIT - Branch Business & Information Systems Engineering

Group Decision and Negotiation, 2023, vol. 32, issue 1, No 4, 75-124

Abstract: Abstract Crowdsourcing holds great potential: macro-task crowdsourcing can, for example, contribute to work addressing climate change. Macro-task crowdsourcing aims to use the wisdom of a crowd to tackle non-trivial tasks such as wicked problems. However, macro-task crowdsourcing is labor-intensive and complex to facilitate, which limits its efficiency, effectiveness, and use. Technological advancements in artificial intelligence (AI) might overcome these limits by supporting the facilitation of crowdsourcing. However, AI’s potential for macro-task crowdsourcing facilitation needs to be better understood for this to happen. Here, we turn to affordance theory to develop this understanding. Affordances help us describe action possibilities that characterize the relationship between the facilitator and AI, within macro-task crowdsourcing. We follow a two-stage, bottom-up approach: The initial development stage is based on a structured analysis of academic literature. The subsequent validation & refinement stage includes two observed macro-task crowdsourcing initiatives and six expert interviews. From our analysis, we derive seven AI affordances that support 17 facilitation activities in macro-task crowdsourcing. We also identify specific manifestations that illustrate the affordances. Our findings increase the scholarly understanding of macro-task crowdsourcing and advance the discourse on facilitation. Further, they help practitioners identify potential ways to integrate AI into crowdsourcing facilitation. These results could improve the efficiency of facilitation activities and the effectiveness of macro-task crowdsourcing.

Keywords: Affordance; Artificial Intelligence; Facilitation; Macro-Task Crowdsourcing (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10726-022-09801-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:grdene:v:32:y:2023:i:1:d:10.1007_s10726-022-09801-1

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10726/PS2

DOI: 10.1007/s10726-022-09801-1

Access Statistics for this article

Group Decision and Negotiation is currently edited by Gregory E. Kersten

More articles in Group Decision and Negotiation from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().