Navigating the Balance Between Urgency and Importance: Exploring Task Prioritization Strategies Across Environments

Abstract

Task prioritization is essential for managing multiple tasks with competing goals, yet psychological research has been limited to basic observations in simplified environments. The current research aims to address this gap by examining how various environmental features influence task management using a novel incentive-compatible game across two experiments. In the game, participants sequenced tasks with different levels of urgency (represented by deadlines) and importance (represented by point values) over multiple rounds, aiming to maximize the number of points earned. In Experiment 1, we manipulated task schedulability (ability to plan task orders in advance) and urgency-importance correlation (degree to which urgent tasks and important tasks conflict). Results showed that planning task orders in advance helped participants better balance urgency and importance, leading to near-optimal performance. In contrast, those who did not plan ahead tended to overprioritize importance and performed worse as a result. In Experiment 2, we manipulated task segmentation (whether tasks were to be completed in segmented parts or as a whole), task comparability (uniform versus varied task lengths), and urgency-importance correlation. We found that task segmentation did not affect urgency or importance prioritization, however it decreased overall performance due to suboptimal task-switching. When task lengths varied, performance declined because participants overprioritized importance, while neglecting urgency and task length. Across both experiments, participants balanced urgency and importance more effectively when these factors conflicted the least, but they tended to prioritize importance over urgency more heavily as the conflict between urgency and importance increased. This research highlights the need for strategies that help individuals better balance importance relative to urgency, particularly in dynamic environments with variable task demands. Limitations and future directions are discussed.