Title data
Teschner, Florian ; Gimpel, Henner:
Crowd Labor Markets as Platform for IS Research : First Evidence from Electronic Markets.
In:
Reshaping society through information systems design : 34th International Conference on Information Systems, ICIS 2013 ; Milan, Italy, 15 - 18 December 2013. Volume 2. -
Red Hook, NY
: Curran
,
2014
. - pp. 976-983
ISBN 978-1-62993-426-6
Abstract in another language
Crowd labor markets such as Amazon Mechanical Turk (MTurk) have emerged as popular platforms where researchers can inexpensively run web-based experiments. Recent work even suggests that MTurk can be used to run large-scale field experiments such as prediction markets in which participants interact synchronously in real-time. Besides technical issues, several methodological questions arise and lead to the question of how results from MTurk and laboratory experiments compare. In this work we provide first insights into running market experiments on MTurk and compare the key property of markets, information efficiency, to a laboratory setting. The results are mixed at best. On MTurk, information aggregation took place less frequently than in the lab. Our results suggest that MTurk participants cannot handle as much complexity as laboratory participants in time-pressured, synchronized experiments.
Further data
Item Type: | Article in a book |
---|---|
Refereed: | Yes |
Keywords: | IS research methodologies; experimental economics; market engineering; market performance |
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 > FIM Research Center Finance & 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: | 14 Sep 2018 07:13 |
Last Modified: | 11 Apr 2022 12:07 |
URI: | https://eref.uni-bayreuth.de/id/eprint/45792 |