In yesterday’s post, I argued that the value of a (online social) network is not only determined by the number of nodes in it, but also in the ability for the network to monetize those nodes. (I believe, however, that this line of reasoning can apply more generally to other “networks.”)

Today, I wanted to introduce the idea that the value of a network also is determined by the strength of the connections between the nodes. And as a result, the degree to which positive “network effects” are experienced depends on the strength of those connections.

Wikipedia states that the “network effect causes a good or service to have a value to a potential customer dependent on the number of customers already owning that good or using that service.” That is, services in which the more people who use it, the more valuable it becomes.

However, not all network effects are created equal because the strength of the connections in various different networks isn’t equal.

On the lower end of value, Amazon experiences network effects when consumers contribute to the user reviews. The more people who contribute reviews, the more valuable the network (and Amazon) becomes vis-à-vis other online retailers. One of the reasons I prefer to use Amazon is due to the fact that a rich set of user-review data is integrated into the product description. (It should be noted that one shouldn’t confuse the network effects that Amazon enjoys with two other distinctly different benefits it possesses, economies of scale and economies of scope). However, the strength of the connection between the users who contribute user reviews is limited and weak. To a single user, it doesn’t matter who contributed the review, but just the fact that many others in the public have.

Contrast that situation with that of Skype or IM networks on the other extreme. The strength of the connection between the nodes is high. A user doesn’t care how many other people use the service generally, just that the specific people who s/he wishes to talk to use the service. In this case, because there is a direct desire to connect to a specific node, the strength of the connection between these nodes is high, and the resulting network effect benefits are higher.

In between these two polar examples are many shades of gray. Some networks necessitate that a general subset of a group of people become a part of the system, but don’t require one person specifically. Examples of these are professional networking sites like LinkedIn. I use this service not because one person specifically is on it, and not because the absolute total number of people using it is large, but because a certain level of the group of people I know are included. This intermediate-level strength of network can take many variant forms, all with corresponding degrees of network effects.

In sum, when assessing a network as a whole and the amount of “network effect” benefits it receives from its users, I believe that the tighter the connections, the greater the value.