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To make the decision model fit support processes, low quality can be modeled as additional process execution, and a high time may directly affect costs.
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So far, we do not have experience on the extent the decision model fits different organizational contexts.
In all cases, the microstimulation effect was significantly smaller for large reward trials compared to small reward trials, fully supporting the results from the pooled data and decision model fitting.
Finally, we conclude that the decision model particularly fits those organizations and business processes that need guidance on BPS.
As we require deep knowledge of the users' process behavior for parameterization, our decision model best fits mature processes that operate in a stable environment.
If the available data are insufficient for specification of each state variable, then ad hoc calibration will likely be needed to allow for inclusion of qualitative decisions about model fit.
As for contexts, the decision model does not fit contexts that are highly restricted by regulations or legislation.
In its current form, the decision model does not fit support processes where time and quality may not affect process demand, but costs.
In this study the decision of overall model fit was based upon four fit indices.
Quantile quantile comparisons between the fit of this decision model and the cumulative Gaussian model described in the previous section indicate a reasonable correspondence between model fits.
We fitted a decision model, the Ratcliff diffusion model, to our two choice reaction time data.
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