Microfoundations of Problem Solving: Attentional Engagement Predicts Problem-Solving Strategies - Laureiro-Martinez, Daniella; Arrieta, Jose Pablo; Brusoni, Stefano (Organization Science, 2023)
Organizations use a plethora of methods and tools to help their members solve problems effectively. Yet the specifics of how individuals solve problems remain largely unexplored. We propose and test a cognitive model of problem solving that integrates dual process theories into the attention-based view. The model suggests that diverse problem-solving strategies emerge in response to how individuals deliberate. Three studies provide observational and causal evidence in support of our model. The first study explores the strategies managers use to solve problems. We use think-aloud protocols combined with content, sequence, and cluster analyses to extract the key differences in how experienced managers solve problems. Two problem-solving strategies emerge from the data: one emphasizes mental activities related to framing, and the other emphasizes mental activities related to implementation. In the second study, we use a mixed factorial experimental design and mouse-tracking analysis to uncover the causal mechanism that explains the emergence of these two strategies. We then retest our hypotheses in a third, preregistered, study. We find that manipulating attention toward mental activities related to framing increases deliberation aimed at restructuring the problem elements. In contrast, directing attention toward mental activities related to implementation increases deliberation on the potential contingencies and consequences of the solution. Our findings provide empirical evidence about how problems are actually solved and support the idea that attentional processes are malleable enough to affect the choice of problem-solving strategies. History: This paper has been accepted for the Organization Science Special Issue on Experiments in Organizational Theory. Funding: For Study 1, funding from EC NEST-2006-PATH-Cul, CID-Cultural and Innovation Dynamics: FP6-043345 is gratefully acknowledged. Supplemental Material: The supplemental material is available at https://doi.org/10.1287/orsc.2019.13213 .