High employee turnover remains a significant challenge for many organizations across diverse industries. Despite high salaries, 66% of senior product managers and 58% of IT program managers report they are considering leaving their jobs. Similarly, 60% of emergency room nurses and 58% of critical care nurses indicate they are planning to quit, despite substantial investments in specialized training. Even teachers, with nearly leaving within five years of their first day, demonstrate the pervasiveness of this issue.

Turnover is costly because replacing departing employees can be difficult. Understanding why employees leave is crucial to developing effective strategies for reducing turnover.

While employees quit for various reasons, our research reveals a specific trigger that may stem from an oversight on the part of employers. Intuitively, one might assume that being assigned numerous “hard tasks” would significantly increase the likelihood of an employee quitting. However, our findings suggest otherwise. It’s not the number of hard tasks but rather the consecutive occurrence of these tasks, creating a “streak,” that triggers quitting. This suggests that managers can significantly reduce turnover by simply rearranging tasks to disrupt these hard task streaks. We refer to this approach as “task sequencing.”

As management scholars, we commonly observe organizations relying on monetary incentives as a motivational tool. However, these incentives are not as effective as often perceived, and they come at a cost. Task sequencing, in contrast, provides a potent avenue for boosting motivation with virtually no expense. In our published in Proceedings of the National Academy of Sciences, we discovered that a task sequencing intervention could dramatically lower the likelihood of employees irrationally quitting (by 22% in our data). This conclusion stemmed from analyzing five years of real-world data involving over 14,000 volunteer crisis counselors at a large organization. Employees in this organization were repeatedly and randomly assigned tasks, categorized as either hard or easy. Each employee typically completed hundreds of tasks during the study, each assigned randomly. This randomization allowed us to establish causality beyond mere correlation. Assigning hard task streaks truly causes employees to quit.

We observed that employees who had previously been assigned streaks of multiple hard tasks “in a row” (as opposed to “not in a row”) were significantly more likely to quit in the future. For instance, employees became 22% more likely to quit if they had previously been assigned a pattern like “easy task, hard task, hard task” (containing a hard task streak) compared to “hard task, easy task, hard task” (lacking a hard task streak). This behavior defies logic because employees were aware of the random task assignment, making the occurrence of hard tasks in a streak irrelevant for deciding whether to quit.

Longer hard task streaks resulted in even higher quitting rates: employees became 110% more likely to quit permanently if they were assigned to complete 8 hard tasks “in a row” compared to 8 hard tasks “not in a row.”

Our research builds on the “peak-end rule,” a discovery by the late Nobel prizewinner Daniel Kahneman and his colleagues. They found that people’s evaluations of past experiences tend to heavily emphasize two psychologically distinct moments: the “peak” (the best or worst moment) and the “end” (the final moment).

Drawing on the peak-end rule, we propose a new concept called the “streak-end rule.” Our key insight is that prolonged streaks of multiple hard tasks in succession can create a “peak” moment in an employee’s psychological experience, disproportionately impacting their decision to quit. Consider an employee assigned one easy task and two hard tasks in the sequence “easy task, hard task, hard task.” The second and third tasks become disproportionately weighted because they form a hard “streak,” generating a negative “peak” or worst moment. Additionally, the third task is overweighted as a negative “end” moment. In essence, employees psychologically overweight the two hard tasks, underweighting the single easy task. As a result, the employee perceives their job as significantly harder than it actually is, leading them to mistakenly quit.

By applying the insights of the streak-end rule, organizations can dramatically reduce turnover rates at practically no cost. Implementing a “task re-sequencing” intervention that avoids assigning “streaks” of multiple hard tasks in a row to any employee—either by rearranging tasks or reassigning them to different employees—would significantly reduce the risk of valuable employees leaving.