Dr. Marcial Losada is the founder and executive director of Meta Learning, a consulting organization that specializes in developing high performance teams. He currently consults with executives and their teams at several corporations in the U.S. and around the world. More information here. His articles are here.
Editor’s Note: Today’s article builds on yesterday’s article by Marcial Losada: Want to Flourish? Stay in the Zone.We are honored to feature these two articles on PPND.
In 1999, I studied business teams and the positivity-negativity ratio (P/N), and learned that higher functioning business teams had significantly higher P/N ratios than low performance teams.
An Example with Low-Performing Teams
We can see that at the Losada line level, the expansion of the emotional field must be at least 48.36% in order to significantly increase the team’s energy for sustainable high performance action. This means that the team must expand the emotional field by about half as much as the lowest performance teams do. Happy, lasting marriages expand their emotional field by 85%, exactly as the very top business teams do. Marriages that end in divorce only manage to expand their emotional field by a meager 15%, which is not enough to generate a sustainable relationship. Disconnection cannot be more than 25.04%; i.e., in a team of 20 people, no more than 5 could be disconnected from the rest of the team. And the process gain by working as a team instead of individually, should be at least 14.56%.
The very first step is to measure these four critical team interaction characteristics in order to determine the most suitable meta learning program: Positivity/Negativity ratio (P/N), expansion of the emotional field, level of disconnection, and process gain. All measures are expressed in percentages, except for the P/N ratio. In table 1 you can see the pre-intervention measures for the four management teams.
Table 1. Pre-intervention measures of 4 management teams in the mining industry
How to Change Low-Performing Teams
After nine months of intensive training following the nonlinear dynamics requirements of the meta learning model; i.e., its nonlinear differential equations that provide guidance by specifying the relationships among the model’s variables, and integrating cognitive, emotional and physical domains of intervention in accordance with these requirements, the objective was surpassed in all the measures for the four teams, as can be seen in table 2.
Table 2. Post-intervention measures of 4 management teams in the mining industry
We can see that the total process gain from pre- to post-intervention for team A was 46.32%. For team B it was 35.06%. Team C gained 40.3%. And the total process gain for team D was 46.9% (see spans from bottom red to top blue in Fig. 2).
Fig. 2. Process gains before and after meta learning training for 4 management teamsOn average, the four teams gained 42.15% from pre- to post-intervention. The emotional field was expanded, on average, from 19.05 to 59.29%; the level of disconnection was reduced from an average of 39.19% to 19.71%. This means that for these teams, with an average of 15 members each, the number of people disconnected was reduced by half: from 6 to 3. Finally, the P/N ratio, which was 1.15 on average at the pre-intervention stage, increased to an average of 3.56; in other words, these teams learned to give 2.41 more positive than negative feedback than they did before the intervention. This was a crucial step to move from low to high performance. It is important to realize that the post-intervention measures are taken from three to six months after the last intervention to make sure that the change introduced by the meta learning program is sustainable, something we guarantee because once a team is able to interact in a complexor dynamics pattern, they will be able to sustain these dynamics over time. Warwick Tucker of the University of Bergen in Norway provided a mathematical proof in 2002 that a complexor will sustain its structure over time.
What Was the Real Effect of this Meta Learning Training?
Beyond these tangible results, an equally compelling narrative is what the CEO of the top management team had to say about the meta learning training:
The team experimented a notable transformation. You untied knots that imprisoned us: today we look at each other differently, we trust each other more, we learned to disagree without being disagreeable. We care not only about our personal success, but also about the success of others. Most importantly, we obtain tangible results. There are a few landmarks in one’s life; this meta learning training was one of them.
The most valuable and enduring lesson these teams learned from the meta learning training was to go from linear management (fixed point dynamics) to nonlinear management (complexor dynamics). One of the keys to switch from one management regime to the other is to keep the P/N ratio within the Losada zone.
Teams that manage to function within the Losada zone incorporate a type of enduring learning that we call meta learning (also described in Want to Flourish? Stay in the Zone), because it represents the ability to dissolve limiting dynamics such as fixed points, and evolve liberating, enriching and lasting dynamics such as complexors. This is why we envision meta learning as a nonlinear process that leads from languishing to sustainable flourishing in relationships and teams.
Fredrickson, B. L. & Losada, M. (2005). Positive affect and the complex dynamics of human flourishing. American Psychologist, 60(7), 678-686.
Losada, M. (1999). The complex dynamics of high performance teams. Mathematical and Computer Modelling, 30(9-10), 179-192. Abstract and order information here.
Losada, M. & Heaphy, E. (2004). The role of positivity and connectivity in the performance of business teams: A nonlinear dynamics model. American Behavioral Scientist, 47(6), 740-765. Abstract and order information here.
Tong, H. (1990). Non-linear time series: A dynamical system approach. Oxford, UK: Clarendon Press.
Tucker, W. (2002). A Rigorous ODE Solver and Smale’s 14th Problem. Foundations of Computational Mathematics, 2, 53-117.