Mechanical Turk and the gig economy: Exploring differences between gig workers
Abstract
Purpose: The purpose of this paper is to provide an investigation of how different types of gig workers engage in the gig economy. Specifically, the authors distinguish between workers who view gig work as primary income (or not) and those workers who view it as a job (or not). Design/methodology/approach: In total, 1,190 Mechanical Turk (MTurk) workers completed surveys across two studies examining whether types of workers differ based on demographic characteristics, utilization of MTurk, why they participate in the gig economy on MTurk (i.e. push and pull factors) and how this impacts life satisfaction. Findings: Workers relying on MTurk as a primary income had lower incomes and spent more time completing large numbers of work units. This group of workers also reported fewer pull factors (e.g. enjoyment) as a reason for working in the gig economy and had lower levels of self-reported current and predicted future life satisfaction. Individuals who view MTurk as a job were more likely to treat MTurk like a job – engaging in online communities and having a regular work schedule. These workers were more likely to report pull factors (e.g. enjoyment and challenge) and did not differ on life satisfaction. Originality/value: The current research contributes to our understanding of MTurk, one of the largest online platforms for gig work, as part of the diverse gig economy and highlights potential areas for future research.
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Algorithmic management has been shown to result in lower life satisfaction.
“lower life satisfaction (Keith, Harms, & Tay, 2019)”
From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Keith et al., 2019
Cites
Evaluating Online Labor Markets for Experimental Research: Amazon.com's Mechanical Turk
Berinsky, A. J., Huber, G. A., & Lenz, G. S. (2012) · Political Analysis · doi:10.1093/pan/mpr057 · Cited by 4,008 · see citing works
Precarious work, insecure workers: Employment relations in transition
Kalleberg, A. L. (2009) · American Sociological Review · doi:10.1177/000312240907400101 · Cited by 2,998 · see citing works
Data collection in a flat world: the strengths and weaknesses of Mechanical Turk samples
Goodman, J. K., Cryder, C. E., & Cheema, A. (2013) · Journal of Behavioral Decision Making · doi:10.1002/bdm.1753 · Cited by 2,242 · see citing works
Reputation as a sufficient condition for data quality on Amazon Mechanical Turk
Peer, E., Vosgerau, J., & Acquisti, A. (2014) · Behavior Research Methods · doi:10.3758/s13428-013-0434-y · Cited by 1,618 · see citing works
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Algorithms as work designers: How algorithmic management influences the design of jobs
Parent-Rocheleau, X., & Parker, S. K. (2022) · Human Resource Management Review · doi:10.1016/j.hrmr.2021.100838 · Cited by 512 · see citing works
Bunjak, A., Černe, M., & Popovič, A. (2021) · Information & Management · doi:10.1016/j.im.2021.103533 · Cited by 127 · see citing works
- Authors
- Keith, M. G., Harms, P., & Tay, L.
- Affiliations
- Bowling Green State University (US)
- University of Alabama (US)
- Purdue University West Lafayette (US)
- Venue
- Journal of Managerial Psychology
- Publisher
- Emerald
- Published
- 2019-05-13
- Volume
- 34
- Issue
- 4
- Pages
- 286-306
- ISSN
- 0268-3946 (ISSN-L)
- OpenAlex
- W2952276635
- Cited by
- 123
- References
- 4
- Citations by year
- 2026: 14 · 2025: 26 · 2024: 23 · 2023: 25 · 2022: 18 · 2021: 14 · 2020: 3
- FWCI
- 17.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Managerial Psychology (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Health Sciences › Health Professions › General Health Professions › Employment and Welfare Studies
- Keywords
- Mechanical Turk, gig economy, gig workers, primary income, push and pull factors, life satisfaction, online labour platforms, employee motivation, online communities, work schedules
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