Self-throttling is a consequence of FairSetup deployment and stands for the idea that team members can throttle expectations to match personal circumstances. For example, if someone works hard for an extended period of time, but then has a child and wants to spend more time at home, or has a tragedy in the family, or simply gets burnt out and decides to take a break. Under the traditional compensation models, there are few options and almost none recognize prior hard work or allow for any sort of systematic throttling – at best an employee is at mercy of their, hopefully empathetic, boss.
However, under FairSetup, one can manage expectations of those around them by doing self-assessments. For example:
In the graph above, the employee starts at level 3 (Professional in this case). Over time, they reach their maximum level. However, just as they reach it, they decide to throttle down to 20% of their capacity and so the impact begins to gradually diminish. After about a year and a half of lower participation, the employee returns to the original position and throttles back up thus causing the impact to begin to grow.
For the model above, time to saturation and desaturation is set to 2 years.
The recommended values for self-throttling are:
|Self: 0%||Not doing any work.|
|Self: 25%||Have a major distraction.|
|Self: 50%||Spending about half of my time on this project.|
|Self: 75%||Mostly working hard, but have some minor distraction.|
|Self: 100%||I am all here|
The reason for using discrete values is to use bucket-based data collection.