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The Presentation of Self in a Virtual but Work-related Environment

From Protagonists to Fools
  • Emmanuelle Vaast
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 236)

Abstract

More and more people take part in virtual environments in which they present a “virtual self”—an online profile that indicates key information about them to other participants and viewers. This research investigates how people present themselves in the virtual yet work-related environments of occupational online forums. To do so, the research analyzes the profiles of more than 300 registered users of an online forum dedicated to issues of interest to bankers. These profiles are interpreted in relation to Goffman’s (1959) seminal ideas of mystification (allowed by the separation between backstage and public action) and presentation of self and of Turkle’s (1995) ideas of multiple, interrelated, online and offline selves. This research builds a grounded categorization of profiles. The four categories of profiles that emerged from the data correspond to clearly distinct ways participants in the online forums present themselves. Over time, two categories have become dominant while another has dwindled. This research holds implications for the understanding of the presentation of self in virtual but work-related environments. It shows how participants in online forums build their virtual self by playing with the mystification inherent of the virtual environment. It also shows an interplay between the virtual and the offline when some participants “de-mystify” their profile. Finally, the increasing prevalence of two categories of profiles suggests that, over time, social norms of presentation of self emerge and condition socially accepted virtual selves in occupational online forums.

Keywords

Social Norm Virtual Environment Impression Management Online Forum Banking World 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© International Federation for Information Processing 2007

Authors and Affiliations

  • Emmanuelle Vaast
    • 1
  1. 1.School of BusinessLong Island UniversityBrooklynUSA

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