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Dealing with the Social Media Polycontextuality of Work

Emmanuelle Vaast, Alain Pinsonneault

Information Systems Research · 2022 · Volume 33 · Issue 4

DOI: 10.1287/isre.2022.1103

Abstract

Existing scholarship on social media provides us with important but incomplete insights regarding how people rely upon social media as they work. Scholarship so far focuses on social media as technologies that enable and constrain people to do certain things. This study proposes a perspective on social media for work that views them not only as technologies that enable people to do certain things, but also as contexts with emerging norms and roles in which people participated. As they do so, people are confronted with opportunities and challenges that are inherent to social media polycontextuality. This study investigates the case of data scientists and how they dealt with the social media polycontextuality of their work. The study reveals that data scientists relied on strategies of enhancing, differentiating, or occasionally reducing their engagement with social media contexts. The study brings novel insights for scholarship on social media and work by unpacking the social media polycontextuality of work and the interconnections in the engagement with multiple social media contexts. It explains how these interconnections manifest in people’s deliberate participation in or temporary withdrawal from diverse and changing social media contexts. This study also contributes to scholarship by highlighting how people deal with an ensemble of social media applications rather than with a single, isolated information technology. History: Manju Ahuja, Senior Editor; Walter Fernandez, Associate Editor. Funding: This work was supported by the Social Sciences and Humanities Research Council of Canada [Grant 435-2015-0241]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1103 .

Keywords

social media, contexts, participation frameworks, social media polycontextuality, qualitative study, grounded theory method, data scientists

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