Facts vs. Stories - Assessment and Conventional Signals as Predictors of Freelancers’ Performance in Online Labor Markets

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dc.contributor.author Holthaus, Christian
dc.contributor.author Stock, Ruth Maria
dc.date.accessioned 2017-12-28T01:52:15Z
dc.date.available 2017-12-28T01:52:15Z
dc.date.issued 2018-01-03
dc.description.abstract This paper investigates how freelancers’ use of signals predicts earnings in online labor markets. Extant literature has questioned the usefulness of some assessment signals to evaluate a freelancer’s quality. We find that conventional signals - signals based on non-verifiable information - can be predictors of higher revenue, when they are based on anecdotes of positive past events (self-promotion). However, mere kindness and flattery towards the customer (ingratiation) is negatively associated with a freelancers’ earnings in OLM. Moreover, we find evidence that the number of tests performed on the platform is significantly associated with higher earnings - with each test that is added to the profile a freelancer-˜s revenue increases by 4.1 %. We base our analysis on a sample of 1065 freelancers using objective financial earnings data, independent codings and survey data.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2018.438
dc.identifier.isbn 978-0-9981331-1-9
dc.identifier.uri http://hdl.handle.net/10125/50326
dc.language.iso eng
dc.relation.ispartof Proceedings of the 51st Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Crowd-based Platforms
dc.subject Online Labor Markets, Freelancer, Conventional Signals, Assessment Signals, Signaling Theory
dc.title Facts vs. Stories - Assessment and Conventional Signals as Predictors of Freelancers’ Performance in Online Labor Markets
dc.type Conference Paper
dc.type.dcmi Text
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