Fair compensation of crowdsourcing work : the problem of flat rates
Salminen, Joni; Kamel, Ahmed Mohamed Sayed; Jung, Soon-Gyo; Mustak, Mekhail; Jansen, Bernard J. (2022-11-28)
Salminen, Joni
Kamel, Ahmed Mohamed Sayed
Jung, Soon-Gyo
Mustak, Mekhail
Jansen, Bernard J.
Taylor & Francis
28.11.2022
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2022112967570
https://urn.fi/URN:NBN:fi-fe2022112967570
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vertaisarvioitu
©2022 Taylor & Francis. This is an Accepted Manuscript of an article published by Taylor & Francis in Behaviour and Information Technology on 28 Nov 2022, available online: http://www.tandfonline.com/10.1080/0144929X.2022.2150564
©2022 Taylor & Francis. This is an Accepted Manuscript of an article published by Taylor & Francis in Behaviour and Information Technology on 28 Nov 2022, available online: http://www.tandfonline.com/10.1080/0144929X.2022.2150564
Tiivistelmä
Compensating crowdworkers for their research participation often entails paying a flat rate to all participants, regardless of the amount of time they spend on the task or skill level. If the actual time required varies considerably between workers, flat rates may yield unfair compensation. To study this matter, we analyzed three survey studies with varying complexity. Based on the United Kingdom minimum wage and actual task completion times, we found that more than 3 in 4 (76.5%) of the crowdworkers studied were paid more than the intended hourly wage, and around one in four (23.5%) was paid less than the intended hourly wage when using a flat rate compensation model based on estimated completion time. The results indicate that the popular flat rate model falls short as a form of equitable remuneration, when perceiving fairness in the form of compensating one’s time. Flat rate compensation would not be problematic if the workers’ completion times were similar, but this is not the case in reality, as skills and motivation can vary. To overcome this problem, the study proposes three alternative compensation models: Compensation by Normal Distribution, Multi-Objective Fairness, and Post-Hoc Bonuses.
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