When a disruptive technology emerges, it is difficult for the inventor as well as for incumbents to figure out the unique business model that will enable to fully realize the economic potential of the new technology. The literature has established that it is hardly possible to design the right business model from the outset, as firms do not have data on markets that do not exist. Rather, firms should conduct real experiments, and progressively refine their business model through “trial-and-error” learning. However, little is said in the current research on how to conduct these experiments. Business model experimentation requires investment. In the case of a disruptive technology, the first investment required for the experimentation of the business model will be the costs generated by the market introduction of the new technology. The decision to launch the new technology is facing two issues: (1) a timing issue: when the success of the business model depends on exogenous sources of uncertainty that are beyond the control of the innovative firm, is it better to launch the technology early, in order to experiment the business model ahead of the competitors, or should the firm better wait to avoid deploying a technology that will never take-off ? (2) a scale issue: is it preferable to launch the technology and experiment the business model on a large scale to improve the fidelity of the test, or should the technology deployment rather take place on a limited scale in order to reduce the cost of the test ? In this paper, we argue that real options can help to support decisions regarding the investment necessary for business model experimentation, and in first place regarding the deployment of the new technology. Compared to conventional discounted cash-flows (DCF) methods, real options are a more dynamic framework, which takes into account the value of flexibility that managers can use as uncertainty gets resolved. We show that the timing issue in the technology deployment decision can be supported by calculating the value of the option to wait. This option takes into account exogenous sources of uncertainty, which affect the efficiency of the business model, yet are beyond the control of the innovative firm. The scale issue can be supported by calculating the value of the option to learn, which takes into account endogenous sources of uncertainty, which the innovative firm can resolve to fine-tune its business model. We illustrate our findings with a case study in the European mobile telecommunications industry, whose dominant business model has been profoundly changed following the introduction of the 3G technology.
