🤖 AI Summary
This study investigates how task-queue ordering rules, worker autonomy, and queue visibility influence performance and quality in service operations. Through a preregistered online experiment integrating dynamically arriving real-world order-picking tasks, behavioral measures, and personality assessments—while controlling for task complexity and arrival dynamics—the research demonstrates that mandating an “easiest-first” sequencing rule significantly enhances accuracy by mitigating workers’ self-selection bias. Conversely, granting workers autonomy over task sequencing reduces overall performance. Although task-arrival notifications trigger short-term efficiency spikes, long-term removal of queue information does not impair sustained performance. These findings underscore the critical role of queue design in human–algorithm collaboration and identify specific personality traits predictive of error propensity.
📝 Abstract
Problem Definition: How a queue of jobs is arranged and presented to workers is an important design problem in service operations. This includes choosing the order in which jobs are performed, how much say workers have in setting that order, and how much queue and arrival information workers receive. Methodology/Results: To better understand how queue design (job ordering, autonomy, visibility) affects worker performance (speed, quality), we run a series of pre-registered online experiments. We use a new, real-effort task in which workers fulfill order-picking jobs of varying complexity that arrive dynamically over time. Our results are as follows: (1) When workers choose their own picking order, we reproduce the field finding that Easy First (EF) ordering is associated with worse performance than First-in-first-out (FIFO), and show that this is mainly due to worker self-selection rather than due to the ordering itself; (2) Exogenously imposed EF ordering improves work quality (picking accuracy) relative to both FIFO and discretionary ordering; (3) Imposing an ordering may reduce speed for the most capable workers; (4) Seeing a new job arrival leads to a short-term productivity burst; however, removing job arrival and queue information altogether does not affect performance in the long term. Managerial implications: Our results provide guidance on which queue design works best for a given performance goal (speed or quality) and worker ability level. We also identify personality measures that can help managers screen for error-prone workers.