Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/50326

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

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Title: Facts vs. Stories - Assessment and Conventional Signals as Predictors of Freelancers’ Performance in Online Labor Markets
Authors: Holthaus, Christian
Stock, Ruth Maria
Keywords: Crowd-based Platforms
Online Labor Markets, Freelancer, Conventional Signals, Assessment Signals, Signaling Theory
Issue Date: 03 Jan 2018
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.
Pages/Duration: 10 pages
URI/DOI: http://hdl.handle.net/10125/50326
ISBN: 978-0-9981331-1-9
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:Crowd-based Platforms



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