Firms are often faced with market and technological uncertainty when trying to renew their business model. The literature suggests that under uncertainty, firms can either develop new business models through commitment, incremental experimentation, or radical experimentation. Experience effects, complexity, and ambiguity influence the appropriateness of these learning approaches. However, no overarching view exists on whether and how these factors interact. Based on a simulation model, we develop propositions regarding the combined influences of complexity, ambiguity, and experience effects on the performance of these learning approaches. We also find that the firm's time perspective matters. The results allow us to refine existing theoretical logic and to delineate the specific conditions under which certain learning approaches outperform others. Furthermore, they provide an explanation as to why investors generally prefer committed businesses. We propose, however, that such commitment is not optimal in the long run and that, in general, firms should consider changing their learning approach over time in order to successfully renew their business model.