The data also establish another surprising fact: Individual reasoning and talent contribute far less to team success than one might expect. The best way to build a great team is not to select individuals for their smarts or accomplishments but to learn how they communicate and to shape and guide the team so that it follows successful communication patterns. In our research we identified three aspects of communication that affect team performance. The first is energy, which we measure by the number and the nature of exchanges among team members.
Normal conversations are often made up of many of these exchanges, and in a team setting more than one exchange may be going on at a time. The most valuable form of communication is face-to-face. The next most valuable is by phone or videoconference, but with a caveat: Those technologies become less effective as more people participate in the call or conference. The least valuable forms of communication are e-mail and texting.
We collect data on those kinds of communication without using the badges. Still, the number of face-to-face exchanges alone provides a good rough measure of energy. Energy levels within a team are not static. For instance, in my research group at MIT, we sometimes have meetings at which I update people on upcoming events, rule changes, and other administrative details.
These meetings are invariably low energy. But when someone announces a new discovery in the same group, excitement and energy skyrocket as all the members start talking to one another at once. The second important dimension of communication is engagement, which reflects the distribution of energy among team members. In a simple three-person team, engagement is a function of the average amount of energy between A and B, A and C, and B and C.
If all members of a team have relatively equal and reasonably high energy with all other members, engagement is extremely strong. When we observed teams making investment decisions, for instance, the partially engaged teams made worse less profitable decisions than the fully engaged teams. This effect was particularly common in far-flung teams that talked mostly by telephone. E-mail and texting are the least valuable. The third critical dimension, exploration, involves communication that members engage in outside their team. Exploration essentially is the energy between a team and the other teams it interacts with.
To measure exploration, we have to deploy badges more widely in an organization. Energy is a finite resource. The more that people devote to their own team engagement , the less they have to use outside their team exploration , and vice versa. But they must do both. Successful teams, especially successful creative teams, oscillate between exploration for discovery and engagement for integration of the ideas gathered from outside sources.
At the MIT Media Lab, this pattern accounted for almost half of the differences in creative output of research groups. A skeptic would argue that the points about energy, engagement, and exploration are blindingly obvious. But the data from our research improve on conventional wisdom. They add an unprecedented level of precision to our observations, quantify the key dynamics, and make them measurable to an extraordinary degree.
We can also state with certainty that in a typical high-performance team, members are listening or speaking to the whole group only about half the time, and when addressing the whole group, each team member speaks for only his or her fair share of time, using brief, to-the-point statements.
The other half of the time members are engaging in one-on-one conversations, which are usually quite short. It may seem illogical that all those side exchanges contribute to better performance, rather than distract a team, but the data prove otherwise. But the badge data showed that these events had little or no effect. A similarly refined view of exploration has emerged in the data.
The best-performing and most creative teams in our study, however, sought fresh perspectives constantly, from all other groups in and some outside the organization. For management tasks that have long defied objective analysis, like team building, data can now provide a foundation on which to build better individual and team performance. This happens in three steps. An energy score of 0. However, using the formulas we developed to calculate energy, engagement, and exploration, we can create maps of how a team is doing on those dimensions, visualizations that clearly convey the data and are instantly accessible to anyone.
The maps starkly highlight weaknesses that teams may not have recognized. Concerned about uneven performance across its branches, a bank in Prague outfitted customer-facing teams with electronic sensors for six weeks. The first two maps below display data collected from one team of nine people over the course of different days, and the third illustrates data collected on interactions between management and all the teams.
Interestingly, at the Soviet-era branches, where poor team communication was the rule, communication outside teams was much higher, suggesting that those teams were desperately reaching out for answers to their problems. Teams at the modern facilities showed high energy and less need to explore outside. According to the bank, those measures helped improve the working culture within all the teams. When we spot such people, we dig down into their individual badge data. Are they trying to contribute and being ignored or cut off? Do they cut others off and not listen, thereby discouraging colleagues from seeking their opinions?
Do they communicate only with one other team member? Do they face other people in meetings or tend to hide from the group physically? Do they speak loudly enough? Perhaps the leader of a team is too dominant; it may be that she is doing most of the talking at meetings and needs to work on encouraging others to participate.
Energy and engagement maps will make such problems clear.
And once we know what they are, we can begin to fix them. Exploration maps reveal patterns of communication across organizations. Time-lapse views of engagement and exploration will show whether teams are effectively oscillating between those two activities. We can create maps that break out different types of communication among team members, to discover, for example, if teams are falling into counterproductive patterns such as shooting off e-mail when they need more face time.
The department had teams of four members each in customer service, sales, support, development, and management. Besides collecting data on in-person interactions with sociometric badges, we gathered e-mail data to assess the balance between high-value face-to-face communication and lower-value digital messages. We did not provide iterative feedback in this project, but if we had, by the end of week one, we would have pointed out three negative trends the group could have corrected: the invisibility of customer service, overreliance on e-mail, and highly uneven communication among groups.
If these issues had been addressed, the problems with the product might have surfaced much earlier, and the responses to them would probably have improved. With maps of the data in hand, we can help teams improve performance through iterative visual feedback. Work we did with a multicultural design team composed of both Japanese and American members offers a good example. Visual data are especially effective at helping far-flung and multilingual groups, which face special communication challenges.
They highlighted that the Japanese members were initially reluctant to speak up, leaving the team both low energy and unengaged. Our data show that far-flung and mixed-language teams often struggle to gel. Cultural norms play a role too. Visual feedback on communication patterns can help. For one week we gathered data on a team composed of Japanese and Americans that were brainstorming a new design together in Japan. Each day the team was shown maps of its communication patterns and given simple guidance about what makes good communication active but equal participation. Day 1: The two Japanese team members bottom and lower left are not engaged, and a team within a team seems to have formed around the member at the top right.
Day 7: The team has improved remarkably. We also told them that the ideal visualization would show members contributing equally and more overall contributions. Now we have the visual tools to likewise improve teamwork through objective analysis. We have seen that by using visualizations as a training tool, teams can quickly improve their patterns of communication.
But does that translate to improved performance? The third and final step in using the badge data is to map energy and engagement against performance metrics. The closer the patterns came to those of our high-performance ideal, the higher productivity rose. In every case the self-reporting on effectiveness mapped to the improved patterns of communication. Through such maps, we often make important discoveries. For each team there, we mapped energy and engagement against average handling time AHT , which we indicated with color. But surprisingly, it also showed that low-energy, low-engagement teams could outperform teams that were unbalanced—teams that had high energy and low engagement, or low energy and high engagement.
The maps revealed that the manager needed to keep energy and engagement in balance as he worked to strengthen them. Visualizations can be used to compare energy and engagement with established performance metrics. The expected team efficiency is based on a statistical analysis of actual team AHT scores over six weeks. Blue indicates high efficiency; red low efficiency.
High-energy, high-engagement teams are the most efficient, the map shows. But it also indicates that low-energy, low-engagement teams outperform teams that are out of balance, with high energy and low engagement, or low energy and high engagement.
This means the call center manager can pull more than one lever to improve performance. Points A and B are equally efficient, for example, but reflect different combinations of energy and engagement. The manager wanted to raise energy and engagement in lockstep. We suggested instituting a common coffee break for each team at the call center. And because all team members took a break at once, interactions were evenly distributed, increasing engagement. The obvious question at this point is, Once I recognize I need to improve energy and engagement, how do I go about doing it? It is mainly used on reciprocating stroking or cycling machines, such as mechanical and hydraulic presses or press brakes.
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