Title data
Gimpel, Henner ; Graf, Vanessa ; Laubacher, Robert ; Meindl, Oliver:
Towards Artificial Intelligence Augmenting Facilitation : AI Affordances in Macro-Task Crowdsourcing.
In: Group Decision and Negotiation.
Vol. 32
(2023)
.
- pp. 75-124.
ISSN 1572-9907
DOI: https://doi.org/10.1007/s10726-022-09801-1
Project information
Project title: |
Project's official title Project's id Projektgruppe WI Digitalisierung No information Projektgruppe WI Künstliche Intelligenz No information Projektgruppe WI Digital Life No information Projektgruppe WI Digital Society No information |
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Abstract in another language
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.
Further data
Item Type: | Article in a journal |
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Refereed: | Yes |
Keywords: | Affordance; Artificial Intelligence; Facilitation; Macro-Task Crowdsourcing |
Institutions of the University: | Faculties > Faculty of Law, Business and Economics > Department of Business Administration Research Institutions Research Institutions > Affiliated Institutes Research Institutions > Affiliated Institutes > Branch Business and Information Systems Engineering of Fraunhofer FIT Research Institutions > Affiliated Institutes > FIM Research Center for Information Management Faculties Faculties > Faculty of Law, Business and Economics |
Result of work at the UBT: | No |
DDC Subjects: | 000 Computer Science, information, general works > 004 Computer science 300 Social sciences > 330 Economics |
Date Deposited: | 07 Feb 2023 09:51 |
Last Modified: | 16 Nov 2023 14:15 |
URI: | https://eref.uni-bayreuth.de/id/eprint/73597 |