Tag Archives: satisfaction

The Central Role of Engagement in Online Communities


(noun) emotional involvement or commitment


You might haven’t noticed but in one way or the other we’ve all interacted on or with an online community. Whether it was while searching for travel routes, computer settings or in a fashion context. Chances are you read some posts until you found what you were looking for and then left the page without contributing. You are not alone in this, 90% of users never or rarely contributes, while 9% contribute 10% of the content and 1% contribute 90% of the content. This is commonly referred to as the 90-9-1 rule. But how can online communities encourage more people to create content and to help recruit others?

This was one of the questions that led Ray and Morris (2014) to conduct their research. More specifically, their goal was to introduce the concept of engagement, which drives pro-social behaviors in the context of open, non-binding online communities. Prior research has extensively recognized the role of engagement in communities, interestingly online community engagement has not been explicitly conceptualized, modeled, measured, or analyzed as a mediating construct in the information systems literature. This paper is the first to do so.

Building on Ma and Agarwal’s (2007) framework the authors propose a model that shows the central role of community engagement and how it relates to different outcomes (Figure 1). Data was collected from 301 users of online communities and structural equation modelling was used to test the proposed model. The developed framework recognizes that online communities are unique socio-technological environments in which engagement succeeds. In particular, members primarily contribute to and re-visit an online community out of a sense of engagement.

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The authors find that members must feel engaged with the online community to actually create content and that members who merely feel satisfied can still help the online community by saying things that might help recruit others. In addition, they found that self-identity verification (the extent to which the way you see yourself matches the way others see you) has an indirect effect on knowledge contribution through engagement. Furthermore,  this paper provide evidence that engagement also mediates the effect between knowledge self-efficacy (the belief that you have the ability and expertise to contribute) and intention to contribute.

The main strength of the paper is its methodology. The authors have applied several models and control variables to ensure valid results. The main managerial implication for community managers is to help members enhance their self-identity, which eventually will lead to more contribution. They can do so by creating signals for members either by letting them choose a badge themselves or by automatically creating signals from prior activities and achievements such as for example”300+ posts on Data Science”.

In conclusion, this Ray and Morris (2014) found evidence that merely satisfaction is not enough to encourage consumers to actively contribute to online communities, but that engagement plays a central role. To get back to the main question raised in the introduction, the key to promoting pro-social behavior (creating content and recruiting others) in online communities is to create the right balance of engagement and satisfaction.



Ray, S., Kim, S. S., & Morris, J. G. (2014). The central role of engagement in online communities. Information Systems Research, 25(3), 528-546.

Ma, M., & Agarwal, R. (2007). Through a glass darkly: Information technology design, identity verification, and knowledge contribution in online communities. Information systems research, 18(1), 42-67.

Which one do you think I should pick ?

Today is Saturday, it is sunny and warm outside. Perfect day to go shopping ! You are obviously going to spend this afternoon with your favorite side-kick. Your mission is to find the perfect outfit that will fit your shape, your personality, and evidently match your preferences.

You finally find these really cool jeans, but you are still hesitant about the top : the blue and black, or the white and gold one ? The retailer comes to you and says that the jeans and the blue and black top would be the perfect match. On the other hand, your friend who has similar tastes as you – so similar you have a couple of clothes in common – votes for the second option.

What would you do ? Who would you trust ? How to make sure which one is the right choice ?

As a matter of fact, when you are browsing the internet, you might encounter the same problem-resolving process.

When a Youtube user surfs on Youtube, they first type the title of the video. On the right side of the video, a section displays other videos. In this section, two types of recommendations are presented :

  • Featured / Related videos :

They are based on the Youtube’s product network ; that is to say that they are based on the site recommendation algorithm only.

  • “Recommended for you” :

These are based on the Youtube’s social network. In fact, another user marked the video you just watched as favoriteas well as another one. Therefore, the second video will be “recommended for you”.

Thus, which of these videos matches users’ preferences the best ?

In a study, participants were asked to watch videos on Youtube, and rate each of them from one star – Poor – to 5 stars – Awesome. One group had access to the product network only – Related-Featured videos – the second group to both product network and the social network – Dual Network – and the third group to user-generated links only – Recommended for you.

What emerges from the study is illustrated in the graph below :

The fact that the second group curve is the lowest shows that finding a liked video takes less time when using the dual network than any of the other networks. Thus, rather than proposing either one or the other, offering both at the same time gives the user more possibilities, more choice, and therefore, more opportunities to reach the right video.

Therefore, the most efficient way for Youtube to satisfy its users, is to offer them as many choices as possible using different methods : the product network as well as the social network.

So, next time you will go shopping and do not know what choice to make, try the clothes on in the fitting rooms ! – or buy both.

References :

Jacob Goldenberg, Gal Oestreicher-Singer, Shachar Reichman, The Quest for Content : How User-Generated Links Can Facilitate Online Exploration, Journal of Marketing Research, August 2012, 462-468, 17p.

Yang Sok Kim, Ashesh Mahidadia, Paul Compton, Alfred Krzywicki, Wayne Wobcke, Xiongcai Cai, Michael Bain,People-to-People Recommendation Using Multiple Compatible Subgroups, AI 2012 : Advances in Artificial Intelligence, 2012.