Increasing participation in online communities: A framework for human-computer interaction

By Jonathan Bishop. Computers in Human Behavior, 2007

362 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Communication, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/503e98185c86b2111425961696f75aaeacf59a7c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Communication

In Human-Computer Interaction

Citations in the corpus

Representations of 'trolls' in mass media communication: a review of media-texts and moral panics relating to 'internet trolling' (2014, background): “This art was lost by the start of the 21st century, and resulted in a number of research papers discussing the prevalence of non-participation, or lurking , and how this could be overcome through greater involvement of elders (Bishop, 2007; Nonnecke and Preece, 2000; Preece et al., 2004).”
Why People Use Social Networking Sites (2009, background): “Some few attempts have been made to understand why people participate [4] or do not participate in SNSs or online communities [2, 5].”
Understanding lurkers in online communities: A literature review (2014, background): “Surveys have been conducted in online communities (Bishop, 2007), email-based discussion list (Nonnecke & Preece, 2000), the social network service (Rau, Gao, & Ding, 2008) and online learning courses (Beaudoin, 2002; Küçük, 2010) to discover the underlying reasons for lurking and methods to encoura”
Influence of personality on travel-related consumer-generated media creation (2011, background): “Since encouraging participation is one of the greatest challenges for any online community provider (Bishop, 2007), and since marketers increasingly depend on market intelligence derived from online opinions about their products, it is important to understand what drives and also prevents online…”
Enhancing consumer engagement in an online brand community via user reputation signals: a multi-method analysis (2018, background): “Relatedly, how do reputation signals affect lurkers—consumers that observe community content but do not actively participate—who are estimated to be between 50 to 90 percent of a community’s users (Bishop 2007; Zhang and Storck 2001)?”