It’s becoming increasingly important for businesses to think about themselves not just in terms of their productivity and efficiency, but also their intelligence. But how do you measure an organization’s intelligence? And with so many groups working remotely, can you measure an online group’s intelligence? It turns out that you can measure and predict group intelligence, and that the same factors affect both face-to-face and online groups.
In a prior study, my colleagues and I took the same statistics techniques used to measure individual intelligence and applied them to measure the intelligence of groups. As far as we know, nobody had ever before asked if groups had an “intelligence factor,” just as individuals do.
We found that there is indeed a single statistical factor for group intelligence that predicts how well the group will perform on a wide variety of tasks. We called this factor “collective intelligence,” and it is only moderately correlated with the average individual intelligence of people in the group. In other words, having a bunch of smart people in the group doesn’t necessarily lead to a smart group. Instead, we found three other factors that predict collective intelligence.
The first was average social perceptiveness or social intelligence of group members. We measured this with a test called “Reading the Mind in the Eyes.” In this test, you look at pictures of other people’s faces and try to guess their emotions. When people in the group are good at that, the group on average is more collectively intelligent.
The second factor was the degree to which people participated equally in a group conversation. When one or two people dominated the conversation, the group was on average less intelligent than when the participation was more evenly spread among the group members.
The third factor that correlated with collective intelligence was the percentage of women in the group. The more women in the group, the more collectively intelligent it was. This factor is mostly explained statistically by the social perceptiveness factor, as we knew before this study that women on average score higher on this measure of social perceptiveness than men. So one interpretation of our results is that a smart group requires a high level of social intelligence, and it may not matter much whether the people who have it are men or women.
Building on that earlier study, we recently extended our work to online groups. We used a similar measure of collective intelligence, but with both face-to-face and online groups. In the online groups, the participants could only communicate through text chat. They couldn’t see or talk to each other.
Surprisingly, we found that the average social perceptiveness of group members was equally predictive of collective intelligence in both face-to-face and online groups. Having people in groups with a high level of social intelligence is just as helpful whether the group meets in person or electronically. This is puzzling, as our study measured people’s ability to read emotions in the eyes, yet online groups can’t see each other’s faces.
So the test must be tapping into a much broader set of skills than we originally thought. It seems to tap into the ability to have accurate theories about what is going on in each other’s minds. People who are good at reading emotions in the eyes also seem to be good at reading emotion in texts and imagining what is going on in others’ minds even though they only see typing.
As for the other two factors, our recent study replicated those findings about gender and group discussions as well.
A key takeaway from this research is that while interpersonal skills have always been important for working successfully in groups in organizations, they will continue to be important—and perhaps even more important—in the electronically connected future.