Studies on organizational learning emphasize the positive effect of slow learning in the long-run. We argue that this result hinges on the selectivity within the organization. Building on March’s (1991) classic model of mutual learning, we design the computational model to study the variation of learning rate effect, based on the different learning conditions by involving selectivity of both organizational code and its members. Our results suggest that the positive effects of slow learning vary depending on the selectivity of each entity. Specifically, we find that benefit of slow learning materializes when organizational code learn selectively. We also test this model across time to address the incentive issues of individuals, regarding to being a slow learner. We find that both learning rate and selectivity of entities should be considered important to seek the optimal learning conditions.