Effectiveness of performance feedback in stimulating user-generated content

Yili Hong, Bin Gu, Gord Burtch, Ni Huang, Chen Liang, Kanliang Wang, Dongpu Fu, Bo Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This study investigates whether and how a platform's provision of performance feedback to users about their prior content contributions can help to stimulate users' subsequent contributions. We draw on social value orientation theory to hypothesize how different framings may impact users' likelihood of producing additional content. We partnered with a major mobile crowdsourcing platform based in China to conduct a randomized field experiment involving the delivery of feedback messages with randomly determined framings, via mobile push notifications. We find that feedback framed either pro-socially or pro-self has a positive effect on content contributions, whereas feedback framed competitively has no such effect. Additionally, we observe differences across genders, such that the positive effects of pro-socially framed feedback are significantly stronger for female users. In contrast, competitively framed feedback is only effective for male users. Our findings provide implications for the design of platform-provided performance feedback to stimulate users' content contribution.

Original languageEnglish (US)
Title of host publication2016 International Conference on Information Systems, ICIS 2016
PublisherAssociation for Information Systems
ISBN (Electronic)9780996683135
StatePublished - 2016
Event2016 International Conference on Information Systems, ICIS 2016 - Dublin, Ireland
Duration: Dec 11 2016Dec 14 2016

Publication series

Name2016 International Conference on Information Systems, ICIS 2016

Other

Other2016 International Conference on Information Systems, ICIS 2016
Country/TerritoryIreland
CityDublin
Period12/11/1612/14/16

Keywords

  • Length
  • Mobile
  • Performance feedback
  • Randomized field experiment
  • Social value orientation theory
  • User-generated content

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