Do you know what everyone else knows?

Often people from different backgrounds find themselves working together in a team. Unless this group worked together before, chances are that no one knows what everyone else knows. This is especially the case if people come from different departments.

But is it really necessary that everyone knows what everyone else knows?

Research on transactive memory systems (TMS), or the collective group knowledge, often assumes that everyone knows what everyone else in the group knows. But from practice we all know that this is usually not the case. Especially if you don’t work in only one group, it is virtually impossible to keep track of who knows what. In other words, there is a certain scale of metaknowledge (the knowledge of who knows what) centralization. In one extreme, everyone knows what everyone else knows. This is called a decentralized TMS. And the other extreme, only one member of the group, the central member, knows what everyone else knows, but no one else does.

There is one more factor to consider when comparing team performance with regard to TMS, the distribution of knowledge. Imagine yourself two groups of people. The first consists of highly specialized experts. Each of the experts knows everything about his or her field and the fields of these experts do not overlap. They have the complete knowledge of a certain subject and nothing is missing, and no one else in the group has this knowledge. This is called connected distribution. Imagine a second group of people with pieces of knowledge from many fields. The knowledge is distributed and interdependent. Because only if connected with the knowledge of other members it creates a whole. This disconnected distribution would be an example of a group of functional specialists.

So, which one results in higher team performance? Is it better that everyone knows what everyone else knows, or that only one central member knows.

Mell et al. (2014) found that a more centralized TMS structure results in higher team performance. Especially when the information within a group is disconnected. The central members will recognize their position. They will see it important to exchange information within the team and pursue it. Therefore, they will act as a catalyst since they will stimulate knowledge exchange. GuruScan visualizes meta knowledge networks in collective knowledge maps, so you do not have to know everyone on the team personally.

If you are trying to create a complete insight in metaknowledge in a team, you are probably wasting your time. Focus your resources rather on developing the catalytic center of the network.

Are you still trying to make everyone know everything?

Do you have a knowledge sharing catalyst?


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