Marcial Losada is the founder and executive director of Meta Learning, a consulting organization that develops high performance teams. As director of the Center for Advanced Research (CFAR) in Ann Arbor, Michigan, he developed a novel approach to study working teams. At CFAR's team work laboratories, time series that showed the interaction patterns of hundreds of business teams were generated that allowed him to develop a nonlinear dynamics model, the meta learning model, which shows the dynamical patterns achieved by high, medium and low performing teams. Performance was defined by three criteria: profitability, customer satisfaction and 360-degree evaluations. The state variables or dimensions of the meta learning model are positivity-negativity, inquiry-advocacy and other-self (external-internal focus). The control parameter of the model is connectivity which reflects the level of attunement and responsiveness that team members have to one another.
Marcial Losada's findings can be summarized as follows: If a team is highly connected, it will tend to maintain an equilibrium between internal and external focus as well as between inquiry and advocacy. It will also keep a positivity/negativity ratio above 2.9 (see Losada line) but not higher than 11.6 (see Losada Zone). If connectivity is low, the team will be more internally focused, it will advocate strongly and its positivity/negativity ratio will be below 2.9. Losada found that high levels of connectivity, a P/N ratio within the Losada Zone, and a balance between internal and external orientation as well as between inquiry and advocacy lead to sustainable high performance in business teams (Losada, 1999; Losada & Heaphy, 2004; Fredrickson & Losada, 2005; Fredrickson, 2009, chapter 7).
Marcial Losada has worked in developing high performing teams at several major corporations in the United States, as well as Europe and Latin America. He is currently developing high performance teams at BHP Billiton and ENDESA-Brasil.
He received a Ph.D. in organizational psychology from the University of Michigan.