Crowdsourcing for Survey Research : where Amazon Mechanical Turks deviates from conventional survey methods

  • Information systems research has started to use crowdsourcing platforms such as Amazon Mechanical Turks (MTurk) for scientific research, recently. In particular, MTurk provides a scalable, cheap work-force that can also be used as a pool of potential respondents for online survey research. In light of the increasing use of crowdsourcing platforms for survey research, the authors aim to contribute to the understanding of its appropriate usage. Therefore, they assess if samples drawn from MTurk deviate from those drawn via conventional online surveys (COS) in terms of answers in relation to relevant e-commerce variables and test the data in a nomological network for assessing differences in effects. The authors compare responses from 138 MTurk workers with those of 150 German shoppers recruited via COS. The findings indicate, inter alia,Information systems research has started to use crowdsourcing platforms such as Amazon Mechanical Turks (MTurk) for scientific research, recently. In particular, MTurk provides a scalable, cheap work-force that can also be used as a pool of potential respondents for online survey research. In light of the increasing use of crowdsourcing platforms for survey research, the authors aim to contribute to the understanding of its appropriate usage. Therefore, they assess if samples drawn from MTurk deviate from those drawn via conventional online surveys (COS) in terms of answers in relation to relevant e-commerce variables and test the data in a nomological network for assessing differences in effects. The authors compare responses from 138 MTurk workers with those of 150 German shoppers recruited via COS. The findings indicate, inter alia, that MTurk workers tend to exhibit more positive word-of mouth, perceived risk, customer orientation and commitment to the focal company. The authors discuss the study- results, point to limitations, and provide avenues for further research.show moreshow less

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Metadaten
Author:Mario Schaarschmidt, Stefan Ivens, Dirk Homscheid, Pascal Bilo
URN:urn:nbn:de:kola-9314
Series (Serial Number):Arbeitsberichte, FB Informatik (2015,1)
Document Type:Part of Periodical
Language:English
Date of Publication (online):2015/04/29
Date of first Publication:2015/04/29
Publishing Institution:Universität Koblenz, Universitätsbibliothek
Release Date:2015/04/29
Tag:Amazon Mechanical Turks; Crowdsourcing; Survey Research; Word-of-Mouth; e-Commerce
Page Number:17
Institutes:Fachbereich 4 / Fachbereich 4
Fachbereich 4 / Institut für Management
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):Es gilt das deutsche Urheberrecht: § 53 UrhG
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