Designed Organizational Search: A Comparative Analysis of Alternative Procedures for Learning from Success

Abstract

Sampling on the dependent variable is unlikely to be an effective way to learn and develop the strategy. Even so, organizations spend millions of dollars on processes such as Appreciative Inquiry that make inferences about how to adapt their strategies, routines, and practices based upon only successful examples. Two techniques that are common to this kind of learning process are searching solely for successful solutions and reframing search problems (e.g., unconditionally positive questions). We build a computational model by formalizing appreciative inquiry and comparing it with other, similar processes to understand their relative effectiveness. We find that the organizations simulated in our computational model almost always improved performance over time, despite learning solely from successful observations. Their relative effectiveness depended on the complexity of the problems, the number of iterations of learning, and how much the learning process preserved variety in potential solutions. These findings suggest that appreciative inquiry may be most effective when people take the cost and complexity of organizational problems into account before engaging in the learning process and adapt the process accordingly. These findings contribute to research on organizational learning by explaining why learners may benefit from structuring the way they communicate as they search, why reframing performance measures may dissolve search problems, and how designed organizational search enables managers to be more deliberate about organizational learning.