A new twist on the gig economy: conducting surveys on Amazon Mechanical Turk

Laurie A. Garrow (), Ziran Chen (), Mohammad Ilbeigi () and Virginie Lurkin ()
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Laurie A. Garrow: Georgia Institute of Technology
Ziran Chen: Georgia Institute of Technology
Mohammad Ilbeigi: Bowling State University
Virginie Lurkin: TU/e, Eindhoven University of Technology

Transportation, 2020, vol. 47, issue 1, No 2, 23-42

Abstract: Abstract There is growing interest in using online outsourcing platforms that are part of the “gig economy” to conduct surveys for academic research. This interest has been driven in part by the belief that compared to traditional survey data collection methods, internet-based marketplaces such as Amazon Mechanical Turk (MTurk) enable one to collect survey data cheaper and faster from a larger, more diverse participant pool. However, many have questioned whether models based on survey data from these online marketplaces are similar to models based on survey data from more traditional platforms. To investigate this research question, we used MTurk and Qualtrics (a traditional market research firm) to survey air travelers. Our results showed that MTurk and Qualtrics respondents had distinct socio-demographic characteristics, but we found no statistical evidence for different air trip characteristics. In our data, proportionately more MTurk respondents were in the younger, single, male, and lower-income categories than for Qualtrics respondents. We found that airline itinerary choice models estimated from the MTurk and Qualtrics survey data were similar, with the key difference related to price sensitivities. Although our results provide evidence that MTurk can be used for travel demand modeling applications, we offer words of caution for others planning to conduct surveys in online marketplaces, particularly for those seeking to recruit more than 1000 participants or for those targeting specific geographic areas.

Keywords: Airline itinerary choice; Survey design; Gig economy; Amazon Mechanical Turk (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11116-018-9962-8

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