Complex compound option models  Can practitioners truly operationalize them?
While there is practitioner interest in real options (RO), there are significant difficulties in practitioner use of complicated RO models, such as compound options pricing models (OPMs) of multistage investments. Drawing upon theories of learning and knowledge, we propose a general framework whereby practitioners can successfully operationalize complex OPMs. From an epistemological perspective, most academic articles on OPMs are essentially propositional knowledge representations that attest to model veracity, and they require a deep background in the analytics of options pricing to comprehend the model which many practitioners lack. A key element of our framework is that the propositional knowledge representation of complex OPMs must be accompanied by a layer of abstraction translating the propositional into procedural knowledge for using the model. Secondly, while this layer of abstraction can be embodied in a software tool for using the OPM, this must be transparently done in order to build trust in the software tool. Thirdly, in the tradition of constructivist learning, using the tool must be illustrated in the context of some contemporary business problem. Finally, a continuous engagement loop must be established which makes routine the application of the RO methodology using this tool. We demonstrate these steps in the context of Geske compound option models for multistage investments. We also show how to apply our tool to the major business problem of enterprise integration. This article therefore provides academics with prescription on fostering practitioner embrace of complex OPMs and practitioners with a tool for operationalizing n-fold compound option models.
compound options real options cost-benefit analysis investment analysis decision-making practitioner relevance