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Chapter 3 Measurement Drives Behavior • 37


            example. “More pounds to love” indicated adding weight was a good
            thing. “Less love per pound” on the contrary, stated that adding weight
            was a bad thing. Managers have an interest in finding and presenting
            the metrics that make their performance look good. It is hardly debated
            that many companies suffer from “gaming the numbers” and “cheat-
            ing the system.” But it would be too easy to blame middle management
            or divide-and-conquer and other forms of opportunistic behavior.
              The problem is not bad people. The real problem is bad perform-
            ance management that makes people behave in opportunistic and polit-
            ical ways. This, in its turn, increases the gap between how people want
            to behave and how they are driven to behave. All these dysfunctional
            behaviors tend to be hidden. So to understand them, and be able to pre-
            dict and perhaps prevent the unintended consequences, we need to
            know where to look for these behaviors. Research on unintended con-
            sequences of performance management dates back more than 50 years. 6
            We can distinguish two basic types of consequences:

              • People impacting on measurement. People trying to play the
                 numbers so that they don’t have to alter their actions.
              • Measurement impacting on people. The metrics put in place
                 drive dysfunctional behavior.

              Table 3.1 shows the unintended consequences of performance man-
                    7
            agement. Perhaps the most well-known example of dysfunctional behav-
            ior is measure fixation. It happens when running the numbers becomes
            more important for managers than running a successful business. An
            example of this is the railway organization that saw its accuracy deterio-
            rate. Accuracy here is defined as the percentage of trains that leave the
            station on time and arrive at their destination on time. Confronted with
            the performance problems on this metric, the operations manager
            decides to widen the margin of the definition. Previously, “on time” was
            defined with a margin of two minutes, one minute before the listed time
            until one minute after. Now the metric is redefined and trains are con-
            sidered to ride on time within a margin of four minutes. To prevent this
            type of unintended consequence from happening, make sure that not
            only the performance indicators are published but also their definitions.
              When measure fixation grows out of control, it can lead to misrepre-
            sentation. This usually looks like a minor means of cheating the system.
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