The Online Behaviour Taxonomy: A conceptual framework to understand behaviour in computer-mediated communication

Linda K. Kaye, Anastasia Rousaki, Laura C. Joyner, Laura A.F. Barrett, Lisa J. Orchard

Research output: Contribution to journalArticle (journal)peer-review

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Abstract

In an Internet-enabled era, we are citizens in a vast array of different online spaces, and the behaviours afforded to these spaces are becoming increasingly complex. Within the study of computer-mediated communication (CMC), there is an assumption that behaviour occurring in CMC is equivalent to that depicted in the communicated message. However, we note that this is not always the case. The purpose of this paper is to elucidate different typologies of “online behaviour” based on our proposed Online Behaviour Taxonomy. Within this, we propose three types of “online behaviour” which are largely distinguishable based on where the behaviour itself originates (online or offline) and how this interacts with internet-enabled technologies. These are: online-exclusive, online-mediated and online-recorded behaviour. Specifically, we assert that the source of behaviour (i.e. whether it occurs online or offline) is currently not explicitly referenced in CMC theory, yet acts as a key indicator to dissect the ambiguity of ‘online behaviour’ as a generalised concept. This is arguably a critical factor associated with user experiences and effects of CMC. We situate this discussion in the wider context of CMC; specifically how factors such as audience effects are differentially relevant to these three types of behaviour. Finally, we outline the emerging conceptual contributions and practical directions which we assert may be influenced by our proposed Online Behaviour Taxonomy.
Original languageEnglish
Article number107443
Pages (from-to)1-11
JournalComputers in Human Behavior
Volume137
Early online date15 Aug 2022
DOIs
Publication statusPublished - 19 Aug 2022

Keywords

  • Online behaviour taxonomy
  • Online behaviour
  • Online interactions
  • CMC
  • Audience effects

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