Abstract
Research on online social networks (OSNs) has focused overwhelmingly on their benefits and potential, with their negative effects overlooked. This study builds on the limited existing work on the so-called ‘dark side’ of using OSNs. The authors conducted a systematic review of selected databases and identified 46 negative effects of using OSNs from the users’ perspective, which is a rich spectrum of users’ negative experiences. This article then proposed nomenclature and taxonomy for the dark side of using OSNs by grouping these negative effects into six themes: cost of social exchange, cyberbullying, low performance, annoying content, privacy concerns and security threats. This study then conducted structured interviews with experts to confirm the sense-making and validity of the proposed taxonomy. This study discusses the confirmed taxonomy and outlines directions for future research.
Highlights
The dark side of information technology (IT), online social networks (OSNs), is important for information systems (IS) research and has become a more salient issue in recent years (Delpechitre et al, 2019; Polites et al, 2018; Turel et al, 2018)
This study sought and found i) why technostress is created, ii) how it varies across IT users, iii) what its negative effects are, and iv) how it can be reduced by organizations
This study aimed to find the dark side of Facebook to understand how Facebook provides the potential for its users to have a positive or negative impact on their target community
Summary
The dark side of information technology (IT), online social networks (OSNs), is important for information systems (IS) research and has become a more salient issue in recent years (Delpechitre et al, 2019; Polites et al, 2018; Turel et al, 2018). There is generally poor theoretical understanding of the dark side of OSN use (Fox & Moreland, 2015). Developing and introducing a taxonomy of the dark side of OSNs which can generate new insights such as advancing the theoretical understanding of the dark side of OSNs is important for IS research.
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