Team Performance training in Geneva · canton of Genève“Let's set an ambitious goal”: when a numerical target holds back a team that is learning
When faced with new software, a new market or a new organisational structure, a manager's first instinct is often to set a demanding numerical target. This instinct is grounded in an established body of research. The question is whether that research applies to a task that no one has yet mastered.
What the research establishes
Edwin Locke and Gary Latham (2002, American Psychologist) synthesise several decades of experiments: a specific, difficult goal is associated with better performance than a vague instruction such as “do your best”. This finding is robust because it rests largely on experimental designs in which goals are randomly assigned. For a familiar task, recommending a demanding target is therefore defensible.
The often overlooked distinction: learning or performing
Ruth Kanfer and Phillip Ackerman (1989, Journal of Applied Psychology) studied the acquisition of a complex simulated air traffic control task. Introducing a performance goal at the very beginning of learning did not help and sometimes proved detrimental: the attention devoted to monitoring the gap between performance and the target was no longer available for understanding the task. The same goal became useful later, once the procedures had been learned. Timing matters as much as the level of the goal.
Which common practices these findings challenge
Dawn Winters and Gary Latham (1996, Group & Organization Management) compared an outcome goal with a learning goal on a simple task and a complex task — for example, finding a specified number of strategies for completing the task. On the complex task, the learning goal was associated with better performance; on the simple task, the outcome goal worked. Setting a production target from the outset when changing tools therefore amounts to applying the right rule to the wrong situation.
What research on side effects adds
Lisa Ordóñez and her colleagues (2009, Academy of Management Perspectives) noted that narrowly defined goals can focus attention at the expense of what is not measured and encourage risk-taking. Maurice Schweitzer, Lisa Ordóñez and Bambi Douma (2004, Academy of Management Journal) observed more false reporting in the laboratory among participants who narrowly missed a goal. Locke and Latham (2009, Academy of Management Perspectives) responded that these risks arise from poor implementation rather than from the goal itself: the debate concerns the conditions, not the principle. (our executive and employee training programmes)
Defensible conclusions and their limits
These studies are based mainly on short laboratory tasks performed individually by students. They establish causal effects under those conditions; applying their findings to a team learning a new tool over several months remains an inference. What can cautiously be stated is that when the method is not yet known, a specific learning goal is a better-supported option than a numerical outcome target, which is better introduced afterwards.
A practical implication to test in Geneva
During the next change of tool or process, replace the volume target for the first few weeks with a precisely formulated learning goal: identify three different ways of handling a typical case and present them to the team. Set the numerical target only once the procedures are stable, then compare the time needed to return to the usual level with that of the previous change. This is the sequence we discuss with management teams in Geneva. To go further: explore the Team Performance training in Geneva in the canton of Geneva, or browse our executive and employee training programmes in Switzerland.
In pictures: Team Performance in Geneva
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