🤖 AI Summary
This study investigates the mechanisms through which iterative and sequential workflows influence innovative behavior and performance. Through three controlled laboratory experiments integrating behavioral observation and performance analysis, the research systematically compares the effectiveness of these workflow types across diverse innovation tasks. It provides the first empirical evidence that iterative processes enhance performance not only in idea generation but also in non-creative tasks, primarily by increasing task-switching frequency and thereby broadening the search space for solutions. The study identifies three critical boundary conditions—tasks requiring extensive exploration, high inter-component coordination, and moderate time pressure—under which iterative workflows significantly boost innovation performance. However, this advantage diminishes as task characteristics shift or over time, offering nuanced insights into the dynamics of innovation search processes.
📝 Abstract
Motivated by the widespread adoption of iterative project management techniques, we study the effects of workflow -- iterative or sequential -- on innovative behavior and performance. We conduct a series of laboratory experiments. Our first experiment shows that, in an open-ended creative challenge, iterative task completion leads to better outcomes than sequential task completion. In the second experiment we show that the advantage of iterative workflow further extends to innovation settings that do not involve idea generation. A key mechanism driving the advantage of iterative work is that it leads to frequent task switching, prompting workers to perform a broader search for the best available solution. In the third experiment we delve deeper into the search process and show that sequential work indeed leads to more myopic idea refinement behaviors, often ending in a (suboptimal) local maximum. Our results suggest that iterative workflow improves performance across multiple, structurally distinct innovation settings. We also identify three boundary conditions. First, iterative workflow helps achieve quick gains, but its performance advantage narrows over time. Therefore, workflow effects are stronger when balanced performance across project components is required, but weaker when excellence in one component can offset poor performance in others. Second, workflow has minimal effect on performance in tasks that do not require the worker to perform broad exploration. Third, workflow effects are minimal when workers complete the easier component first.