Unpacking support types in online health communities

An application of attraction-selection-attrition theory

Babak Abedin, Shadi Erfani, David Milne, Annette Beattie , Kate Fenerty

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

1 Citation (Scopus)

Abstract

Online communities are increasingly becoming part ofthe healthcare ecosystem, as they allow patients,family members and carers to connect and support each otherat any time and from any location.This support can take many forms, including information, advice, esteem support and solidarity.Prior research has identified the Attraction-Selection-Attrition Theoryas a promising framework for modelling and explaining how participants join, participate,and leave organizations in general (and online communitiesspecifically), and how the actions of individuals effectthe organizationas a whole.However, it has not previously beenapplied specifically to online health communities (i.e. those that focuson physical and/ormental health).We propose to gather empirical evidence from a large online community that provides support for Australians effected by cancer. In doing so, we hope to developevidence-based policies and procedures for growing, maintaining and moderating these communities.
Original languageEnglish
Title of host publicationPACIS 2019 - Secure ICT Platform for the 4th Industrial Revolution
Subtitle of host publicationProceedings of the 23rd Pacific Asia Conference on Information Systems
EditorsDongming Xu, James Jiang, Hee-Woong Kim
Place of PublicationXian, China
PublisherAssociation for Information Systems
Number of pages8
Publication statusPublished - 2019
Externally publishedYes
Event23rd Pacific-Asia Conference on Information Systems, PACIS 2019 - Xi'an, China
Duration: 8 Jul 201912 Jul 2019

Conference

Conference23rd Pacific-Asia Conference on Information Systems, PACIS 2019
CountryChina
CityXi'an
Period8/07/1912/07/19

Keywords

  • Online health communities
  • ASA Theory
  • Online support
  • Cancer patients

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