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Connectivity, the control parameter in a nonlinear dynamics model of team performance is mathematically linked to the ratio of positivity to negativity (P/N) in team interaction. By knowing the P/N ratio it is possible to run the nonlinear dynamics model that will portray what types of dynamics are possible for a team. These dynamics are of three types: point attractor, limit cycle, and complexor (complex order, or "chaotic" in the mathematical sense). Low performance teams end up in point attractor dynamics, medium perfomance teams in limit cycle dynamics, and high performance teams in complexor dynamics.
Keywords: positivity; connectivity; team performance; nonlinear dynamics
Positive organizational scholars have made an explicit call for the use of non-linear models stating that their field "is especially interested in the nonlinear positive dynamics... that are frequently associated with positive organizational phenomena" (Cameron, Dutton, & Quinn, 2003, pp. 4-5). This article answers this call by showing how a nonlinear dynamics model, the meta learning (ML) model, developed and validated against empirical time series data of business teams by Losada (1999), can be used to link the positivity/negativity ratio (P/N) of a team with its connectivity, the control parameter in the ML model. P/N was obtained by coding the verbal communication of the team in terms of approving versus disapproving statements. In the ML model, positivity and negativity operate as powerful feedback systems: negativity dampens deviations from some standard, while positivity acts as amplifying or reinforcing feedback that expands behavior. We will demonstrate how these P/N ratios determine the types of dynamics possible for a team. By running the ML model, one can observe that different levels of connectivity create different nonlinear dynamics that, in turn, are associated with different levels of performance in business teams. Hence, by making explicit the relationship between P/N and connectivity, we will show that P/N can also be associated with the performance of these teams. This finding has important implications for the emerging field of positive organizational scholarship. In addition, the advantage of using P/N as a proxy for connectivity is that measures of P/N are much easier to generate than the measures of connectivity used in the ML model. We will define these measures later in the article, after providing the necessary context.
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