This field study research evaluates the viability of applying an option-based risk management (OBRiM) framework, and its accompanying theoretical perspective and methodology, to real-world sequential information technology (IT) investment problems. These problems involve alternative investment structures that bear different risk profiles for the firm, and also may improve the payoffs of the associated projects and the organization's performance. We sought to surface the costs, benefits, and risks associated with a complex sequential investment setting that has the key features that OBRiM treats. We combine traditional, purchased real options that subsequently create strategic flexibility for the decision maker, with implicit or embedded real options that are available with no specific investment required provided the decision maker recognizes them. This combination helps the decision maker to both (1) explicitly surface all of his or her strategic choices and (2) accurately value those choices, including ones that require prior enabling investments. The latter permits senior managers to adjust a project's investment trajectory in the face of revealed risk. This normally is important when there are uncertain organizational, technological, competitive, and market conditions. The context of our research is a data mart consolidation project, which was conducted by a major airline firm in association with a data warehousing systems vendor. Field study inquiry and data collection were essential elements in the retrospective analysis of the efficacy of OBRiM as a means to control risk in a large-scale project. We learned that OBRiM's main benefits are (1) the ability to generate meaningful option-bearing investment structures, (2) simplification of the complexities of real options for the business context, (3) accuracy in analyzing the risks of IT investments, and (4) support for more proactive planning. These issues, which we show are more effectively addressed by OBRiM.
As real options analysis (ROA) is being applied to increasingly complex information technology (IT) investment problems, a concern arises over the use of heuristic ROA models that are simpler to apply but can produce overvaluations. A good example is the application of a heuristic nested variation of the Black-Scholes (BS) model to the evaluation of interrelated IT investments as nested options. This particular heuristic BS model could overvalue by more than 100 percent. Using a binomial model that is custom-tailored to a generic IT investment embedding nested options as the "baseline," we identify conditions under which the degree of overvaluation of this heuristic BS model is severe and unpredictable. Moreover, upon examining the structure of the custom-tailored binomial model, we identify the reason for overvaluation and derive a more accurate nested variation of the BS model. These findings should serve as a cautionary message about the use of untested heuristic ROA models.