A categorisation framework for a feature-level analysis of social network sites

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O'Riordan, Sheila
Feller, Joseph
Nagle, Tadhg
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Taylor & Francis
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Social media (SM) have enabled new forms of communication, interaction, and connectivity that affect individuals on a personal and professional level. But SM is a broad term that encompasses a wide range of technologies with both distinct and shared capabilities. In addition, while there is an agreed-upon definition of these systems, a comprehensive list of features and their affordances does not exist. Hence, this study sought to create a feature-level categorisation framework for analysing the use of social network sites (SNS). This categorisation was undertaken using the concept of affordances, which framed the high-level characteristics as well as distinct SNS features, to better understand the divergence in SNS capabilities and inform the study of different types of SM. The framework was created from an analysis of the literature on SNS affordances and a system investigation into three types of SNS (Facebook, YouTube and Twitter). The comprehensive review was undertaken using two families of SNS affordances (social and content affordances) identified in the literature to categorise and compare the platforms. The study reveals a diverse collection of features which afford behaviour in six areas of activity: profile building, social connectivity, social interactivity, content discovery, content sharing and content aggregation. Finally, the framework provides a basis from which the usage and management of SM within organisations can be more rigorously investigated.
Social media , Social network sites , Affordances , Categorisation framework , System features
O’Riordan, S., Feller, J. and Nagle, T. (2016) 'A categorisation framework for a feature-level analysis of social network sites', Journal of Decision Systems, 25(3), pp. 244-262. doi: 10.1080/12460125.2016.1187548
© 2016 Informa UK Limited, trading as Taylor & Francis Group. This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Decision Systems on 16 June 2016, available online: http://www.tandfonline.com/10.1080/12460125.2016.1187548