Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/41377

A Large-scale Analysis of the Marketplace Characteristics in Fiverr

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dc.contributor.author Maity, Suman Kalyan
dc.contributor.author Jha, Chandra Bhanu
dc.contributor.author Kumar, Avinash
dc.contributor.author Sengupta, Ayan
dc.contributor.author Modi, Madhur
dc.contributor.author Mukherjee, Animesh
dc.date.accessioned 2016-12-29T00:45:31Z
dc.date.available 2016-12-29T00:45:31Z
dc.date.issued 2017-01-04
dc.identifier.isbn 978-0-9981331-0-2
dc.identifier.uri http://hdl.handle.net/10125/41377
dc.description.abstract Crowdsourcing platforms have become quite popular due to the increasing demand of human computation-based tasks. Though the crowdsourcing systems are primarily demand-driven like MTurk, supply-driven marketplaces are becoming increasingly popular. Fiverr is a fast growing supply-driven marketplace where the sellers post micro-tasks (gigs) and users purchase them for prices as low as $5. In this paper, we study the Fiverr platform as a unique marketplace and characterize the sellers, buyers and the interactions among them. We find that sellers are more appeasing in their interactions and try to woo their buyers into buying their gigs. There are many small tightly-knit communities existing in the seller-seller network who support each other. We also study Fiverr as a seller-driven marketplace in terms of sales, churn rates, competitiveness among various subcategories etc. and observe that while there are certain similarities with common marketplaces there are also many differences.
dc.format.extent 10 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 50th 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 Fiverr
dc.subject Marketplace Characteristics
dc.subject Supply-driven marketplace
dc.title A Large-scale Analysis of the Marketplace Characteristics in Fiverr
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2017.223
Appears in Collections: Data Analytics and Data Mining for Social Media Minitrack


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